Hybrid and Solar Vehicles

International Workshop on

Hybrid and Solar Vehicles

University of Salerno, Italy

November 5-6, 2006 www.dimec.unisa.it/WHSV

Proceedings

Copyright © 2006

PREFACE

The growth of mobility has had a positive effect on prosperity and quality of life, but its negative impact on the environment and the erosion of non-renewable resources are becoming more and more visible. As a consequence, the attention toward the sustainable mobility is rapidly increasing, spreading from specialists to final users and to public opinion. In last decade, the hybrid electric vehicles have emerged as a valid mid-term solution to reduce fuel consumption and carbon dioxide emissions. Their integration with photo-voltaic sources may give a further contribution toward the mitigation of fossil fuels depletion, global warming and climate changes. Despite these promising perspectives, there is a certain lack of systematic research on the integration of hybrid vehicle technology with solar sources. This Workshop is dedicated to hybrid and solar vehicles, with particular emphasis on the combined use of these two approaches. These proceedings include 13 papers, from Hungary, France, Italy, Romania, Spain, Turkey and United States. Most of the research presented is conducted in an academic context, also in cooperation with industry and research centres. The papers cover several aspects of hybrid and solar vehicles. The actual trends and the opportunities related to the integration of electric vehicles with photo-voltaic and, more generally, with renewable sources are presented in the first paper. Five papers deal with modelling, design and control of hybrid solar vehicles, also caring for profitableness of such vehicles. Other five papers concern hybrid electric vehicles: hybridization of a small vehicle for urban transportation and of a 4WD parallel vehicle, control of super-capacitors, HEV real-time control and performance testing. Other two papers are devoted to photovoltaic sources for automotive applications, concerning MPPT modelling and power interfaces. I would thank all the Authors for their dedication in preparing excellent technical papers, the members of Scientific Committee for their cooperation in paper review and my colleagues at the University of Salerno for their help in the Workshop organization. We acknowledge the financial and operative support of University of Salerno to this Workshop, co-sponsored by the Technical Committee “Automotive Control” of International Federation of Automatic Control and by SAE Naples Section. We also recognize the significant impulse given to the studies on hybrid solar vehicles by the European Community in supporting the Leonardo Project “Engine Conversion Systems and Their Enviromental Impact”, with sponsorship of Automobile Club Salerno, Lombardini, Saggese and Province of Salerno. The Workshop Chair Gianfranco Rizzo

Chair

Prof. Gianfranco Rizzo, DIMEC, University of Salerno (I), [email protected]

Scientific Committee

I.Arsie, DIMEC, University of Salerno (I)

M.Basset, UHA, Mulhouse (F)

J.Bokor, BUTE, Budapest (HU)

E.Chiappini, University of L’Aquila (I)

G.Gissinger, UHA, Mulhouse (F)

L.Guvenç, ITU, Istanbul (TR)

Y.Guezennec, OSU, Columbus (USA)

L.Guzzella, ETH, Zurich (CH)

I.Ionita, Univ. of Galati (RO)

T.Peter, BUTE, Budapest (HU)

C.Pianese, DIMEC, University of Salerno (I)

G.Rizzo, DIMEC, University of Salerno (I)

G.Rizzoni, OSU, Columbus, Ohio (USA)

G.Spagnuolo, DIIIE, University of Salerno (I)

Organizing Committee

I.Arsie, DIMEC, University of Salerno (I)

G.Rizzo, DIMEC, University of Salerno (I)

M.Sorrentino, DIMEC, University of Salerno (I)

G.Spagnuolo, DIIIE, University of Salerno, (I)

CONTENTS S.E.Letendre Prometheus Institute for Sustainable Development, Vermont (USA)

USHERING IN AN ERA OF SOLAR-POWERED MOBILITY 1

Zs. Preitl (1), P. Bauer (1), J. Bokor (2) (1) Budapest University of Technology and Economics, Dept. of Transport Automation, Hungary (2) Computer and Automation Research Institute, Budapest, Hungary

FUEL CONSUMPTION OPTIMIZATION FOR HYBRID SOLAR VEHICLE 11

P. Bauer (1), Zs. Preitl (1),P. Gáspár (2), Z. Szabó (2), J. Bokor (2) (1) Budapest University of Technology and Economics, Dept. of Transport Automation, Hungary (2) Computer and Automation Research Institute, Budapest, Hungary

CONTROL ORIENTED MODELLING OF A SERIES HYBRID SOLAR VEHICLE 19

A.Boyali (1), M.Demirci (1), T.Acarman (2), L.Güvenç (1), B.Kiray (3), M.Yildirim (3) (1) Istanbul Technical University, Mechanical Engineering Dept., Istanbul, Turkey (2) Galatasaray University, Fac.of Engineering and Technology, Istanbul, Turkey (3) Ford-Otosan, Product Development, R&D Department, Kocaeli, Turkey

SIMULATION PROGRAM AND CONTROLLER DEVELOPMENT FOR A 4WD PARALLEL HEV 27

I.Arsie, R.Di Martino, G.Rizzo, M.Sorrentino DIMEC, University of Salerno, Italy

A MODEL FOR A PROTOTYPE OF HYBRID SOLAR VEHICLE 35

G.Petrone (1), G.Spagnuolo (1), M.Vitelli (2) (1) DIIIE, University of Salerno, Italy (2) DII, Seconda Università di Napoli, Aversa (CE), Italy

A MODEL OF MISMATCHED PHOTOVOLTAIC FIELDS FOR SIMULATING HYBRID SOLAR VEHICLES

43

I.Ionita, D.Negoita, S.Paraschiv, I.V. Ion University of Galati "Dunarea de Dos", Romania

THE PROFITABLENESS OF HYBRID SOLAR VEHICLES 49

C.Boccaletti (1), G.Fabbri (1), F.M.Frattale Mascioli (2), E.Santini (1) (1) Department of Electrical Engineering, University of Rome “La Sapienza”, Italy (2) Department INFOCOM, University of Rome “La Sapienza”, Italy

TECHNICAL AND ECONOMICAL FEASIBILITY STUDY OF A SMALL HYBRID VEHICLE FOR URBAN TRANSPORTATION

57

D.Paire (1), M.Becherif (2), A.Miraoui (1) (1) L2ES, UTBM, Belfort (cedex) 90010, France (2) SeT, UTBM, Belfort (cedex) 90010, France

PASSIVITY-BASED CONTROL OF HYBRID SOURCES APPLIED TO A TRACTION SYSTEM 63

G.Rousseau (1,2), D.Sinoquet (1), P.Rouchon (2) (1) Institut Français du Pétrole, 92852 Rueil Malmaison, France (2) Centre Automatique et Systèmes, École des Mines de Paris, Paris, France

HYBRID ELECTRICAL VEHICLES: FROM OPTIMISATION TOWARD REAL-TIME CONTROL STRATEGIES

71

N.Caccavo, G.Carbone, L.Mangialardi, L.Soria Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Italy

PERFORMANCE TESTING OF HYBRID VEHICLES IN BARI DOWNTOWN 79

M. Cacciato, A. Consoli, G. Scarcella, A. Testa Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Catania, Italy

HYBRID VEHICLES WITH ELECTRICAL MULTI ENERGY UNITS 87

A.Cid-Pastor (1,3), L.Martínez-Salamero (2), C.Alonso (1), G.Schweitz (3), R.Leyva (2) (1) LAAS-CNRS, Laboratoire d’Analyse et des Architectures des Systèmes, Toulouse, France (2) ETSE Universitat Rovira i Virgili / Dept. Eng. Electrònica, Elèctrica i Automàtica, Tarragona, Spain (3) EDF R&D / LME Department, Moret sur Loing, France

IMPEDANCE MATCHING FOR PV GENERATOR 93

USHERING IN AN ERA OF SOLAR-POWERED MOBILITY

Steven E. Letendre, Ph.D.

Green Mountain College, Poultney, VT & The Prometheus Institute for Sustainable Development, Cambridge, MA, USA

[email protected]

Abstract: Modern mobility, for both humans and commodities, relies almost exclusively on fuels derived from petroleum. At the same time the world is experiencing soaring demand for mobility, environmental and resource constraints have become increasingly acute. This article discusses the role that electric drive, initially in the form of hybrid electric vehicles, can play in addressing the mobility challenge. This article discusses the opportunity that electric drive vehicles create to use solar and wind power for transportation. The potential of the emerging vehicle integrated PV concept is discussed, along with the importance of connecting cars to the electric grid. Keywords: electric vehicles, solar energy, renewable energy systems, electric power systems

1. MOBILITY IN THE 21ST CENTURY

Human progress is tied to advances in mobility. Societies adept at harnessing technology to reduce the travel times to distant lands successfully gained access to new resources, allowing wealth creation opportunities beyond which local resources allowed. The process accelerated dramatically as fossil fuels were employed to provide even greater opportunities to move people and commodities across great distances. Today, mobility is a commodity for which demand is linked closely to income. Specifically, increases in demand for highway travel and air travel in a country tracks closely growth in national income. Figure 1 provides data on per capita vehicle miles travelled (VMT) and per capital air travel from 1960 to 2004 in the US. During this timeframe per capita income grew from $13,800 to $38,856 while per capita VMT more than doubled and per capita domestic air travel quadrupled. Based on the experiences in the US, per capita VMT took approximately 30 years to double, while per capita domestic miles flown doubled in just ten years.

-

2,000

4,000

6,000

8,000

10,000

12,000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2004

Year

per c

apita

VM

T

0

5

10

15

20

25

per c

apita

air

trave

l

Per Capita VMT

Per Capita Miles Flown(domestic)

Fig. 1. Mobility trends in the US: Per capita

vehicles miles travelled and per capita domestic air travel, 1960 to 2004 (Sources: US Bureau of Economic Statistics and the US Bureau of Transportation Statistics)

As incomes in the developing world rise, demand for mobility likewise increases in these regions. Myer and Kent (2003) in their book New consumers: The influence of affluence on the environment highlight the rapid increase in demand for personal automobiles occurring in the developing world and in countries as a new consumer class emerges. They argue that over 1 billion of these new consumers will

Workshop "Hybrid and Solar Vehicles", November 5-6, 2006, University of Salerno, Italy 1

soon have an aggregate spending capacity, in purchasing power parity terms, to match that of the US. Recent data suggests that China is rapidly expanding its automobile manufacturing capabilities; annual passenger production grew from 100,000 vehicles in 1991 to 2.3 million in 2004—a 28 fold increase (Worldwatch Institute, 2006). We have reached an apex in global mobility. The shear volume and pace of movement, of both humans and commodities, on this planet is incomprehensible. The 3.7 trillion passenger-kilometers of air travel in 2005 equals over four and a half million round trips from the Earth to the Moon (ICAO, 2005). What made this level of mobility possible, and how much longer can it be sustained? This critical question is addressed in the next section of the article. 1.1 Petroleum and transportation: resource

constraints, the environment, & supply risks Petroleum-derived fuels, such as gasoline for vehicles and jet fuel for modern aircraft, provide over 97% of primary energy for transportation. Of the 80 million barrels used globally each day in 2003, approximately one half are consumed for transportation. The US Department of Energy’s Energy Information Administrations (EIA) predicts that global oil consumption will reach 118 million barrels per day by 2030 (EIA, 2006). In sum, transportation is entirely dependant on a single source of energy—petroleum—and its consumption for transportation purposes is predicted to rise by 47% in twenty-five years. Most of this increase will come from rising demand for transportation in non-OECD countries (EIA, 2006). The state of modern transportation systems is extremely precarious. Relying exclusively on petroleum as a source of energy for transportation creates significant risks, the most important of which is resource limits. Volumes have been written about the so called peak oil phenomenon, which suggests that global oil production peaks and subsequently enters a prolonged period of decline. While oil does not “run out” many predict that prices rise dramatically in the face of rising demand and declining production (Simmons, 2005). While the timing of peak oil is the subject of debate, it’s generally accepted that it will occur within the first half of this century. The use of petroleum for transportation is a factor linked to global climate change. The combustion of fuels for transportation causes carbon dioxide emissions, the primary pollutant contributing to global warming, into the atmosphere. Approximately 25% of global emissions of carbon dioxide come from the transport sector. In addition, transport related emissions are one of the fastest growing categories, which is likely to increase the share of total carbon emissions coming from the transport sector.

A number of recent scientific studies suggest that global climate change is occurring more rapidly than scientists predicted and is already having negative impact on ecosystems across the globe. Governments and non-governmental organizations worldwide are calling for dramatic reductions in carbon dioxide emissions to minimize further warming of the Earth and the associated consequences of rising sea levels, more severe weather patterns, and negative ecosystem impacts. Clearly, efforts are needed to reduce the transport-related emissions of carbon; this can only be accomplished by either reducing the amount of travel, increasing the efficiency of the vehicle fleet, shifting toward alternative fuels, or some combination there of. Supply risks are an additional concern linked to the transport sector’s exclusive reliance on oil as a primary energy source. Roughly one-third of global oil production comes from the politically volatile Middle East (EIA, 2006). Furthermore, this region is home to the largest known oil reserves, thus the region will become increasingly important as a global supplier. The region is currently enmeshed in several armed conflicts, including the conflict between the US and Iraq. Terrorist attacks on key ports and escalating regional violence could cause significant supply shocks.

2. TOWARD SUSTAINABLE MOBILITY The scope of the mobility challenge is daunting. The issue must be addressed on multiple fronts, from smart planning to reduce the need for travel by automobiles to the development of new vehicle technologies. The remainder of this article focuses specifically on options to reduce the light vehicle fleet’s dependence on petroleum-derived fuel sources. This is achieved through either improving fuel economy and/or using alternative fuels. Progress has been made in these areas, but virtually all vehicles commercially available today run primarily on either gasoline or diesel fuel. In the US, the primary mechanism for regulating vehicle fuel economy is the Corporate Average Fuel Economy (CAFE) standard, established at the national level. These standards remain unchanged since 1985 at 27.5 miles per gallon (mpg). Europe is further along in addressing the mobility challenge with more developed mass transit systems and a much more efficient light vehicle fleet than that found in the US. The search for viable alternative fuels has focused on biofuels, with interest in biofuels surging in recent years. Brazil is often held up as a successful example of large-scale biofuel development, meeting 20% of its transport fuel requirements with ethanol derived from sugar cane. The development of flex-fuel vehicles in the US is gaining momentum, which provides a vehicle owner a choice of energy options


to meet their transportation needs. For example, some automobile manufacturers are building vehicles that operate on biofuel blends like E85—a blend of 85% ethanol and 15% gasoline. Biofuels offer the potential to reduce our dependence on gasoline for the light vehicle fleet, but the potential is limited. There is much debate about the energy balance of biofuels and the appropriateness of using arable land to produce energy crops as apposed to food. It is unlikely that biofuels will emerge as a replacement for gasoline as a transport fuel, although they could serve to displace a small portion of gasoline and diesel fuel for the light vehicle fleet. Much effort is being directed at producing fuel cells for mobile applications, fuelled with onboard compressed hydrogen. Fuel cell vehicles running on compressed hydrogen are viewed by some as the ultimate means to achieve sustainable mobility. In recent years, however, some have questioned the over emphasis on research and development in to fuel cell vehicles and their potential to reduce carbon emissions in the short-term. It is becoming increasingly clear that hydrogen-powered fuel cells vehicles face a number of technical and economic challenges that will likely take decades to address (Morris, 2003). In a 2004 report prepared by the US-based Center for Energy and Climate Solutions for the National Commission on Energy Policy concluded, “We believe that the most plausible vehicle of the future is a plug-in hybrid running on a combination of low-carbon electricity and a low-carbon biomass-derived fuel.” (Center for Energy and Climate Solutions, 2004) 2.1 The hybrid electric vehicle revolution Hybrid electric vehicles (HEV), using both an internal combustion engine and electric motor, achieve dramatic improvements in fuel economy. Commercially available HEVs boast fuel economy ratings of over 50 mpg. For example, the most popular hybrid, the Toyota Prius, achieves a fuel economy rating of 60 mpg highway and 51 mpg city. Consumers now have several HEV options to choose from, and their popularity among the car-buying public is increasing. Virtually every major automobile manufacturer is manufacturing, or plans to in the near future, HEVs. In 2005, HEVs reached 1.2% of new cars sold in the US, more than doubling the number sold in the prior year. Toyota is the leading manufacturer of HEVs, globally selling over 50% of all hybrids purchased in the US in 2005. The evolution of HEVs to allow charging from the electric grid, so called plug-in hybrids (PHEV), is assumed by many to be desirable—some may argue inevitable. Ultimately, the economics of displacing gasoline with electricity should drive consumer demand for PHEVs. The cost of electricity to drive a vehicle the same distance as one gallon of gasoline is

equal to approximately $1—or even less if off-peak electricity prices are assumed (Denholm and Short, 2006). Furthermore, as discussed later in this article, PHEVs could potentially generate revenue for the vehicle owner by providing grid support services. Combined, these value propositions could serve to usher in an era of advanced vehicles with dramatic reductions in gasoline use and tailpipe emissions. A growing, national movement to bring PHEVs to the market has emerged in the US, bolstered by the undeniable economic and national security benefits that result from displacing gasoline with electricity. One highly-visible, grass-roots campaign, called Plug-In Partners, seeks to demonstrate to the major automobile manufacturers that a national market exists for flexible-fuel PHEVs; dozens of businesses, utilities, municipal governments, and environmental groups have joined the Plug-In Partners campaign. While there are no commercially available PHEVs on the market, a number of prototypes have been built and tested. The most established PHEV program is housed at the University of California Davis, where Professor Andrew Frank works with students designing and building prototype PHEVs. A second development project involves collaboration between the Electric Power Research Institute and DaimlerChrysler. They produced, and are in the process of testing, several prototype plug-in hybrid vans using the Sprinter platform. Two start-up firms plan to offer conversion kits for current generation hybrid electric vehicles to allow grid charging of the on-board battery pack. These conversions kits offer the potential to almost double an HEV’s fuel efficiency rating to 100+ miles per gallon by increasing the size of the battery storage system and installing the hardware and controls to allow charging from the electric grid. 3. HYBRIDS AND RENEWABLES: EXPLORING

THE POTENTIAL As the vehicle fleet moves toward electric drive, initially in the form of HEVs, the opportunity for renewables, beyond biofuels, to serve as an energy source for the transport sector emerges. This opportunity is greatly enhance when vehicles connect to the grid to charge an onboard battery pack. The remainder of this article explores this opportunity from the emerging vehicle integrated concept (VIPV) to the role that wind can play in powering grid-connected cars. Hybrids electric vehicles with the capability to recharge from the electric grid dramatically reduce the needed liquid fuels for transportation. Studies have found that most vehicles could meet the vast majority of their daily commute with a PHEV designed with a 40 mile all electric range. Thus, PHEVs can exploit wind and solar as a fuel source and at the same time dramatically reduce liquid fuel requirements. It becomes more realistic for biofuels to meet the lower liquid fuel requirements needed as


the vehicle fleet relies to a greater degree on electricity. 3.1 The Solar Hybrid Electric Vehicle In 2003, the author presented the vehicle integrated photovoltaic (VIPV) concept to an American audience at the annual meeting of the American Solar Energy Society. The paper titled, Vehicle integrated PV: A clean and secure fuel for hybrid electric vehicles argued that HEVs create an opportunity for PV to serve as an energy source for the transport sector. Until recently, PV has not been considered a viable energy source for vehicles. Some experiments were conducted using PV for electric vehicle (EV) charging, but efforts to commercialize have stalled due to the perceived lack of market acceptance for these types of vehicles. Other efforts to deploy PV for transportation have taken place at a variety of university research centers, where teams of students and faculty build vehicles powered solely from solar. These vehicles are designed and built to compete in solar car races such as the World Solar Challenge, which began in Australia in 1987. These vehicles were never intended for commercial production, the futuristic look and design of these experimental vehicles would not likely appeal to mass markets. Since the 2003 conference, the author learned of a variety of projects to advance the VIPV concept. Researchers at the University of Queensland in Australia are developing a commuter hybrid vehicle with PV integrated in to the body panels. An engineer in Canada installed a 270 watt solar array on the roof of his Toyota Prius, increasing the mileage by approximately 10%. Even the major auto manufacturers are eyeing the VIPV opportunity, with both Ford, and its close corporate partner Mazda, displayed hybrid vehicles with modest amounts of VIPV at recent auto shows. The author produced a second article on the topic highlighting recent VIPV activities, which appeared in the May/June 2006 edition of Solar Today. In October of this year, the French specialty vehicle manufacturer Venturi Automobiles announced plans to offer the first commercially available solar hybrid sports car called the Astrolab. The company also produces an urban electric commuter vehicle called the Eclectic. The 3-seater vehicle has solar PV integrated on to the roof of the vehicle. Venturi claims that this is the first energy-autonomous vehicle available to the public.

Pic. 1. PV integrated Toyota Prius, Lapp

Renewables LTD, 2005

Pic. 2. Venturi Automobiles’ Astrolab, the first

commercially available PV integrated hybrid

Pic. 3. Venturi Automobiles’ Eclectic, the first

energy autonomous electric urban commuter vehilce

Recently, Taiwan’s PV cell manufacturer E-Ton Solar announced a joint venture with several partners, including Yulon Nissan Motor Co., Ltd. to develop PV products for the car market. The joint venture began with the manufacturing of PV modules for car sunroofs.


3.2 Design Considerations for Solar Hybrids Given current HEV designs, VIPV could serve to enhance the overall efficiency of the vehicle, but only provide a small portion of the vehicle’s energy requirements. In this context, VIPV is similar to regenerative breaking, which, through converting the kinetic energy lost in breaking to electrical energy, serves to enhance the overall efficiency of an HEV. A number of design and engineering considerations could serve to increase PV’s role in fuelling a new generation of solar hybrid vehicles The key parameters dictating VIPV’s ability to displace gasoline for transportation are the quantity of PV in watts integrated on to the body panels and the efficiency of the vehicle drivetrain. The amount of PV that can be integrated on to a vehicle is a function of the available space and the efficiency of the PV technology deployed. Venturi Automobile’s Astrolab mentioned above contains 3.6 m2 of PV integrated on to the vehicle. Measurements of the available surface area of a number of conventional vehicles suggest available surface areas of between 3.5 m2 to 5.5 m2 (Letendre et al., 2006). Figure 2 indicates potential PV in watts for three different scenarios of available surface by PV conversion efficiencies.

-

200

400

600

800

1,000

1,200

5% 6% 7% 8% 9% 10%

11%

12%

13%

14%

15%

16%

17%

18%

19%

20%

PV Conversion Efficiency

wat

ts V

IPV

3.5 m24.5 m25.5 m2

Fig. 2. VIPV watts potential: surface area vs. PV

sunlight to electricity conversion efficiency As Figure 2 illustrates, the sunlight to conversion efficiency of the PV technology deployed in VIPV applications is an important parameter. While flat plate silicon PV has high conversion efficiencies, thin film PV may be better suited for VIPV applications. Again referring back to Venturi Automobile’s Astrolab, the vehicle uses high efficiency monocrystaline PV cells to achieve 600 watts of PV on the available 3.6 m2 of surface area. Copper indium gallium diselenide (CIGS) solar cells, which are not yet fully commercial, offer both advantages of flexibility like other thin film PV technologies, but with much higher conversion efficiencies. One US company, DayStar Technologies, is nearing commercial-scale production of a CIGS PV product on flexible steel. Generally, the US is leading in the development of the next generation PV technology, which should be predominantly flexible thin films. It should be noted that the onboard PV capacity may not necessarily be constrained by the available

surface area on the vehicle’s body panels, but flexible PV could be used to design retractable solar shades that could be deployed when the vehicle is parked to provide additional PV capacity for daytime charging. The efficiency of the vehicle drivetrain determines the number of solar miles obtained from any given VIPV system. Current hybrids, like the Toyota Prius have all electric efficiencies in the 156 watt-hours per kilometer range. Figure 3 illustrates solar miles for a 500 watt VIPV system in a region with an average of 4 sun hours per day for total annual PV generation of 710 kWh.

Fig. 3. VIPV watts potential: surface area vs. PV

sunlight to electricity conversion efficiency Advances in the use of lightweight materials for vehicles will serve to increase the potential solar miles delivered from a VIPV system. However, even today’s commercially available hybrid can benefit from VIPV. Initial VIPV applications will provide incremental improvements in vehicle efficiency, but the future potential is much greater. The Leonardo Project, sponsored by the European Commission, aims to train a new generation of engineers in sustainable transportation focused initially on designing and building a solar hybrid. This project, and other like it, will serve to advance knowledge on these concepts and ultimately achieve advanced designs that dramatically improve existing technologies and approaches. Battery storage devices are a critical enabling technology for the solar hybrid revolution. While many advances have been made in battery technology, reductions in price and improvements in performance are needed to produce commercially viable solar hybrid vehicles. A promising new battery technology was unveiled at the September 2006 California Air Resources Board Zero Emission Vehicles Symposium. Navada-based Altairnano announced a new lithium ion battery system called NanoSafe™, which replaces graphite as the electrode materials with nano-titanate materials (www.altairnano.com). The company claims that this new materials solve the thermal runaway problem with conventional lithium ion batteries, and offer significant improvements in cycle life and delivers optimum energy/power balance in the high power region, which is critical for hybrid and electric vehicle applications.

- 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000

50

100

150

200

250

wat

t-hou

rs /

kmAnnual Solar Kilometers

Honda Insight

Toyota Prius

SUV


3.3 Plug-In Hybrids Facilitates the Use of Wind for

the Transport Sector While both conventional HEVs and PHEVs can adopt a VIPV strategy allowing for the use of solar for transportation, only plug-in hybrids facilitate the use of wind power for transportation purposes. Wind power is the fasting growing new source of power generation world-wide. In the US alone the American Wind Energy Association estimates that over 3,000 MW of new wind will go on line in 2006. Globally, estimates of installed wind power capacity reached 60,000 MW in 2005 (Worldwatch Institute, 2006). Wind power is a clean and renewable source of power generation that will continue to expand in the coming years. The intermittent nature of wind power creates challenges for developers seeking to integrate wind into electric grids and wholesale markets. At low wind power penetration rates intermittency is less of an issue; however, as wind plays an increasingly important role in the global supply mix, intermittency will need to be addressed. The variability of output from wind farms creates challenges given the existing electric industry structure, which is characterized by scheduled flows of power from sources to sinks. The cost and environmental characteristics, however, are sufficiently compelling that regulations have been devised to facilitate wind power integration in to the electric supply mix. The variability of wind power can be understood in discrete categories based on increasingly longer time intervals that characterize the market strategy that is needed to manage the variability as more and more wind appears on the electric network. These categories are:

• Minute to hour variability, addressed through regulation markets, intra-hour adjustments, or spinning reserves.

• Hour to day, addressed through operating reserves (spinning and non-spinning reserves)

• 1-4 days, dispersion of wind resources with transmission, operating reserves, load management, and dedicated storage (Kempton and Tomic, 2005a)

Recent analyses suggest that the emergence of PHEVs and other electric vehicles could serve to address the intermittency challenge associated with wind and other intermittent resources like solar (Letendre et al., 2002; Kempton and Tomic, 2005a, and Denholm and Short, 2006). In one of these studies Kempton and Tomic (2005a) calculate that that electric vehicles with onboard battery storage and bi-directional power flows could stabilize large-scale (one-half of US electricity) wind power with 3% of the fleet dedicated to regulation for wind, plus 8–38% of the fleet providing operating reserves or storage for wind.

At a minimum, the nature of PHEV charging complements the intermittent nature of wind power. Given the high periods of non-use of vehicles, PHEVs represent a new source of load, unlike critical loads like computers and other information technologies, which doe not require a constant flow of power for re-charge. The charging of PHEVs can be modulated as the power production from a wind farm varies. This serves to address the first tear of intermittency (variability) described earlier. I envision new power contracts between PHEV owners and developers of wind farms. The complementary nature of wind power and PHEVs creates an opportunity to further enhance the environmental character of PHEVs through wind power charging. To address the second and third tiers of wind power variability described earlier, PHEVs would require reverse flow capabilities. This concept has become widely known as the vehicle to grid (V2G) concept, which is covered extensively in the next section of this article. Millions of PHEVs connected to the electric grid would represent a very large aggregate energy storage resource. Figure 4 indicates the amount of storage that would be connected to the grid for PHEVs with various electric only ranges (from 20 to 60 miles) by the number of vehicles. Even at small penetration rates in the new car market PHEVs could offer a significant storage capacity to address wind power’s longer duration variability.

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

Millions of V2G Vehicles

MW

h S

tora

ge P

oten

tial PHEV60

PHEV40PHEV30PHEV20

Fig. 4. PHEV energy storage potential It’s quite possible that VIPV, wind power charging, and ethanol or biodiesel could create the first mass market, mobility solution that is 100% renewable. This mobility system becomes even more attractive when understood in the context of the emerging vehicle to grid concept. Next, I turn to this topic and describe the benefits that are possible as the transport and electric power sectors converge.


4. V2G: INTEGRATING THE TRANSPORT AND ELECTRIC POWER SECTORS

As the vehicle fleet moves toward electric drive, initially in the form of HEVs, interesting synergies can be exploited between the transport and the electric power sectors when a bi-directional grid interface is built. In aggregate, grid-connected cars would represent a potentially large and highly reliable power resource to the electric power sector. This opportunity was first explored by Kempton and Letendre in a 1997 article published in Transportation Research-D. The light vehicle fleet and the electric power system represent two massive energy conversion systems, which evolved in isolation from each other over the past century. The electric power system relies on thousands of generating units which convert stored energy (chemical [coal, natural gas, oil], mechanical [hydro and wind], and nuclear) in to alternating current that flows across a massive interconnected transmission and distribution grid to final end users. In contrast, the light vehicle fleet coverts petrochemical energy to rotary motion and then to travel. A massive petroleum, refining, and transport infrastructure exists to support the light vehicle fleet’s energy needs. The electric power industry is unique in that the product, electricity, is produced and consumed at the same time. There is virtually no storage in the system; except for pumped hydro in select locations. Grid operators must continuously match supply and demand by turning on and off generators in response to demand. In contrast the light vehicle fleet requires storage, given that its fuel must be mobile and thus is carried onboard in a storage container. As the light vehicle fleet migrates toward electric drive, storage energy in onboard batteries serves to supplement the stored energy in the vehicle’s fuel tank. Electric generators are designed for high duty cycles, in the US average utilization rates of the nation’s generating assets reaches 60%. In contrast, as mentioned above, vehicles are in use approximately 5% of the time. While electric generators can take minutes or hours to deliver power to the grid, electric drive vehicles could deliver power to the grid virtually instantaneously. In aggregate these complementary characteristics of the electric power sector and the light vehicle fleet offer a compelling reason to evaluate the integration of these systems as vehicle technology migrates toward electric drive. Through a bi-directional interface, grid-connected cars could deliver power when called upon by a central grid operator. Figure 5 illustrates schematically the vehicle to grid (V2G) concept. Advances and cost reductions in wireless communications would allow a central operator to dispatch stored energy in vehicles upon demand. In Figure 5 the Independent System Operator (ISO) is delivering a dispatch signal to those vehicles

connected to the grid and prepared to deliver power at a moments notice.

Fig. 5. Schematic of vehicle to grid concept

(Kempton and Tomic, 2005a) Even at small fractions of the vehicle fleet, electric drive vehicles could represent a very large power resource. At 10 kW per vehicle, one million vehicles represent 10,000 MW of available V2G power; the current global vehicle fleet is estimated to be over 600 million vehicles (Worldwatch Institute, 2006). 4.1 V2G Research Finding The author knows of just one V2G demonstration project (Brooks, 2002). The demonstration project was conducted by a California-based electric vehicle development company AC Propulsion, in conjunction with the California Independent System Operator (ISO). AC Propulsion produces the only V2G capable electric vehicle drivetrain. For the demonstration project a Volkswagen Beetle was converted to a pure electric vehicle outfitted with AC Propulsion’s bi-directional charger and a communication link with the California ISO. They successfully demonstrated the remote dispatch of power from a parked electric vehicle in response to a signal from the ISO. Most of the research to date on V2G involves modelling and economic analyses. One comprehensive study, for which the author was involved, was funded by the California Air Resources Board. Although no technical barriers were discovered in the research, a number of key issues were identified that bear on the economic value of V2G power services. Research on this topic suggests that V2G capable cars are best suited to provide grid services that require a rapid response, but our used for a short duration. The limited onboard energy storage of an electric drive vehicle is not suited for providing base-load power. The most promising markets for V2G power fall under the heading of ancillary services—services purchased by grid operators to maintain system reliability. The two most valuable ancillary services in the US are for regulation (frequency response) and spinning reserves. Economic analyses demonstrate that a single vehicle can generate hundreds of dollar annually providing these services (Letendre and Kempton, 2002).


A second important issue for V2G capable cars, which determines the potential revenue from providing grid services, is the power output that can be sustained by a vehicle providing ancillary services. Kempton and Tomic (2005b) identify three key factors that limit the amount of power a grid-connected car can deliver back to the grid. These include the on board vehicle electronics, capacity of the plug circuit, and energy storage capacity and state of charge when the vehicle is plugged in to provide grid services. A PHEV’s vehicle’s power electronics should not create a binding limit on the amount of power that can be exported to the grid. PHEVs require high power components for acceleration and to optimize vehicle performance. The electric drivetrain developed and manufactured by AC Propulsion mentioned earlier provides 80 amps in either direction, allowing 19.2 kW of power output. Thus, the critical factors dictating the reverse power potential come down to the capacity of the plug circuit and the size and state of charge of the PHEV’s battery pack. Given the evidence on the V2G potential today, the next logical step would be a large-scale demonstration project. A fleet of say 100 electric drive vehicles equipped with a bi-directional charger could serve to resolve some issues that would give the private sector more confidence in pursuing the V2G business opportunity. In the end, the revenue that V2G could generate would help to overcome the price premium for the first-generation plug-in hybrids or pure electric vehicles, thus ushering in a new era of clean, flexible fuel vehicles. As experience is gained and the price of electric drive vehicles declines, their use in providing peak power and storage for intermittent renewables is more likely. Furthermore, an increasingly fleet of V2G capable vehicles could eventually enhance the overall reliability of the grid and support a more environmentally sound electric supply mix.

5. CONCLUSION As we enter the early stages of the 21st Century, society has reached an apex in mobility. The global economy is poised precariously on continues flows of people and goods, made possible by an abundant and cheap source of energy—oil! Recent events suggest that this critical resource is no longer abundant and cheap. In 2006, petroleum reached record prices on international exchanges of over $70 per barrel. Some of the world’s most renowned petroleum geologists are warning that we are quickly approaching the point at which we have extracted approximately one half of the existing oil reserves buried deep in the Earth crust—the so called peak oil event. These, and other critical geopolitical events, suggest that society must rapidly pursue the development of alternative means of transportation to maintain even

a portion of the mobility we have come to rely upon in this modern ear. It’s becoming increasingly clear that electric drive will play a central role in the future vehicle fleet. Already, today hybrid electric vehicles (HEVs) have gained commercial success. Many groups are actively pursuing the logical evolution of HEVs to allow charging from the electric grid. Others are focused on hydrogen as the primary energy carry for transportation, fuelling a future fleet of fuel cell vehicles. Regardless of the technology that dominates the future, vehicle will rely increasingly on electric drive and contain significantly more onboard battery storage than today’s fleet of internal combustion engines. This new era of electric drive vehicles allows for renewables, beyond biofuels, to serve as an energy source for the light vehicle fleet. Vehicle integrated PV and grid-connected cars charging from wind power become real possibilities as hybrid electric vehicles emerge as viable alternatives to internal combustion vehicles. There is tremendous momentum in this direction as research organizations, governments, and private industry seek to solve our immanent mobility crisis. A French specialty automobile company plans to offer the first commercial solar hybrid to consumers. E-Ton Solar, a major PV manufacturer, has entered a joint venture to develop products specifically for the car market. Finally, the V2G concept is the ultimate vision whereby the transport and electric power sector converge and reap tremendous efficiencies while improving reliability, reducing pollution, and delivering greater energy security to those nations with the foresight to seize this opportunity.

REFERENCES Brooks, A. (2002). Vehicle-to-grid demonstration

project: Grid regulation ancillary service with a battery electric vehicle. Report to the California Air Resources Board.

The Center for Energy and Climate Solutions. (June 2004) The car and fuel of the future: A technology and policy overview, Prepared for the National Commission on Energy Policy, Washington, DC.

Energy Information Administration (EIA), US Department of Energy. (2006). International energy outlook 2006, Washington, DC.

International Civilian Aviation Organization (ICAO). (28 July 2005). World air passenger traffic to continue to expand through to 2007, press release, Montreal.

Kempton, W and J. Tomic. (2005a). V2G implementation: From stabilizing the grid to supporting large-scale renewable energy. J. Power Sources, 144, 280-294.

Kempton, W and J. Tomic. (2005b). Vehicle to grid fundamentals: Calculating capacity and net revenue. J. Power Sources 144, 1, 268-279.


Kempton, W., J Tomic, S. Letendre, A. Brooks, and T. Lipman. (2001). Electric drive vehicles-battery, hybrid, and fuel cell-as resources for distributed electric power in California, University of California Davis, ITS-RR-01-03.

Kempton, W., and S. Letendre. (1997). Electric vehicles as a new power source for electric utilities. Transportation Research-D, 2, 157-175.

Letendre, S. R. Perez, and C. Herig. (May/June 2006). Solar vehicles at last?. Solar Today, Vol. 20, No. 3, 26-29.

Letendre, S., R. Perez, and C. Herig. (2003). Vehicle integrated PV: a clean and secure fuel for hybrid electric vehicles. Proceedings of the 2003 American Solar Energy Society Annual Conference, Boulder, CO.

Letendre, S and W. Kempton. (2002). V2G: a new model for power?. Public Utilities Fortnightly, 140, 16-26.

Letendre, S., R. Perez, and C. Herig. (2002). Battery-powered, electric-drive vehicles providing buffer storage for PV capacity value. Proceedings of the 2002 American Solar Energy Society Annual Conference, Boulder, CO.

Myers, N. and J. Kent. (2004). The new consumers: The influence of affluence on the environment, Island Press, Washington, DC.

Morris, D. (2003). A better way to get from here to there: A commentary on the hydrogen economy and a proposal for an alternative strategy, The Institute for Local Self-Reliance, Minneapolis, MN.

Simmons, M. (2005). Twilight in the desert: The coming Saudi oil shock and the world economy, Wiley & Sons, Inc, Hoboken, New Jersey.

Worldwatch Institute. (2006). Vital signs 2006 – 2007: The trends that are shaping our future, W.W. Norton & Company, Inc., New York, NY.


FUEL CONSUMPTION OPTIMIZATION FOR HYBRID SOLAR VEHICLE

Zs. Preitl*, P. Bauer*, J. Bokor**

* Budapest University of Technology and Economics, Dept. of Transport Automation, H-1111 Budapest, Bertalan L. u. 2., Hungary

Email: [email protected], [email protected], [email protected] ** Computer and Automation Research Institute,

H-1518 Budapest, Kende u. 13-17, Hungary

Abstract: Hybrid electric vehicles (HEVs), having multiple main energy sources, are an attractive alternative to conventional vehicles. The paper presents a study on minimizing the energy consumption in a series hybrid solar vehicle (HSV). First a description of the series HSV is given, after which two control strategies are presented for fuel consumption optimization. The first control strategy is dynamic programming (DP) which is used to obtain a global optimum for fuel consumption. The second control algorithm is Model Predictive Control, using the MPC Toolbox of Matlab. Both strategies are tested through simulations. Keywords: hybrid solar vehicles (HSV), control strategies, dynamical programming (DP), Model Predictive Control (MPC)

1. INTRODUCTION Hybrid electric vehicles (HEVs), having multiple main energy sources, are an alternative to conventional vehicles. More and more importance is dedicated to research in this field of alternative vehicles. These energy sources are the conventional fuel tank and a battery, delivering both chemical and electrical energy. If a photovoltaic panel is also added, a Hybrid Solar Vehicle (HSV) is obtained. HSVs can be seen as a transition from conventional vehicles to fully electric vehicles. The architecture of HSVs can be different, depending on the requirements imposed. Basic drivetrain structures for HSVs are: series, parallel, series/parallel and complex hybrids. Since the target of the research is optimization of fuel consumption in case of urban drive cycles, a series architecture was chosen for this study, this proving to be optimal in this case. A basic diagram of the series HSV is depicted in Figure 1. The first control strategy is based on dynamic programming (DP), which is actually used to obtain a global optimum for fuel consumption. The reference signal consists of several urban cycles. The result is an input sequence of battery nominal power values. Since DP is not a feasible solution for practical implementation due to its computational time, an alternative control strategy consists in Model Predictive Control (MPC), implemented using the MPC Toolbox of the Matlab environment. Simulations were performed and presented in the paper for both

strategies. To test and compare simulation results, standardized drive cycles had been defined in the literature, this paper focuses the simulations mainly on the so-called New European Driving Cycle (NEDC) and on the Federal Urban Driving Schedule (FUDS) which were presented in detail in (Bauer et al., 2002).

Fig.1. Basic diagram of a series HSV

2. FUEL CONSUMPTION MINIMIZATION USING DYNAMIC PROGRAMMING

Optimal control of the series HSV was first achieved in this paper with dynamic programming. This is based on


Bellman’s principle which says that: “The parts of an optimal trajectory are all optimal trajectories”. This allows one to make calculations on a specific problem backward in time, with the assumption of optimal trajectory. The result of dynamic programming calculations is the optimal input sequence applicable to the system to achieve control goals. Dynamic programming gives the global optimal solution of the problem. Unfortunately this solution needs a priori knowledge of the reference signal and disturbances on the entire time horizon considered in the calculations. This means that, the results of a dynamic programming solution can mainly be used just as a reference optimal solution to be compared with other control methods, such as MPC control in this paper. The other problem with dynamic programming is the time consuming calculations which prevent its application in real time solutions. For the used HSV model with NEDC drive cycle, the calculation of the optimal solution on a 1200 sec time horizon needed one hour on a PC with AMD 64 Athlon 3000+ processor and 1 GB DDR 400 RAM. In the following subsections the problem formulation, solution with dynamic programming and the results of this global optimal solution are discussed.

2.1 PROBLEM FORMULATION AND DYNAMIC PROGRAMMING SOLUTION

The control goal of a HSV is the minimisation of fuel consumption over the whole time horizon considered in calculations. This can be achieved by proper switching (balancing) between the energy sources. In a HSV the electric motor’s (EM) power needs can be satisfied from the photovoltaic (PV) panel, battery and electric generator (EG). This means that one can optimize the use of this three energy sources. The electric power from PV panel depends on sun insolation and cell temperature (see Bauer et al. 2006). Unfortunately, one cannot control these parameters, so PV power cannot be a control variable, however it can improve the fuel economy of the vehicle. The system layout used for dynamic programming solution is depicted in figure 2. The notations used can also be seen in figure 2. The fuel consumption optimization can be achieved by the proper use of the EG and the battery, while satisfying drive power needs and sustaining battery state of charge (SOC), considering the whole time horizon. The power balance of the system is described by the following equation:

PVbnege PPPP ++= (1)

On the right side, electric generator power and battery nominal power are the control variables.

electric motor power can be calculated from drive power need, considering the characteristics of the EM. The controller can influence and .

egP bnP

eP dP

egP bnP

Figure 2. System layout for dynamical programming

However, if one gives , is determined by equation 1. So the optimal solution of the control problem can be generated by the calculation of the sequence in time.

bnP egP

bnP

In dynamic programming this can be achieved by a backward calculation from end of the drive cycle and final value of the battery SOC. The start and end values of battery SOC must be the same (charge sustaining strategy). Of course, the drive cycle for the HSV must be a priori known. It the paper there were used the NEDC and FUDS drive cycles, with given constant insolation and temperature on PV panel. The charge sustainability gives limits on battery SOC in time. A diamond shaped limit set can be calculated for every vehicle and drive cycle as, it is presented in figure 3.

Figure 3. Battery SOC bounds with NEDC drive cycle,

1 kW/m2 insolation and 25°C cell temperature

The calculation are performed considering the possible SOC values at every time step, which can be achieved according to the constraint, , and the minimal and maximal allowed SOC values. The minimal and maximal SOC values are 0.6 and 0.8 respectively, from (Musardo et al. 2005). Both the upper and lower limits are described with three sections. These are the following:

)()0( endSOCSOC ≡


1. Upper: the maximum possible SOC value which can be achieved from SOC(0) using maximum battery charge

2. Upper: the maximum allowed SOC value 3. Upper: the maximum SOC value from which

SOC(end) can be achieved using maximum battery discharge

1. Lower: the minimum possible SOC value which can be achieved from SOC(0) using maximum battery discharge

2. Lower: the minimum allowed SOC value 3. Lower: the minimum SOC value from which

SOC(end) can be achieved using maximum battery charge

Of course for these calculations the maximum and minimum nominal battery charge powers have to be known for every time instant. The minimum power (discharge power) is given by the limits of the battery. The maximum power (charge power) is given by the limits of the vehicle and can be calculated from (1):

maxmax egPVebn PPPP −−= (2)

In this form, reaches a negative value (if and

are assumed to be positive) which has to be considered in the battery calculations. In the presented example is positive in EM driving mode and negative in EM braking mode, which fits the calculations in (2).

bnP egP

PVP

eP

The calculated minimum and maximum powers for the case from figure 3 can be seen in figure 4.

Figure 4. Maximum and minimum battery power, with

drive power need (NEDC drive cycle, 1 kW/m2 insolation and 25°C cell temperature)

In figure 4 it can be seen that the maximum charge power (negative according to (2)) has a minimum point (in absolute value) where the drive power need is maximal. After calculating the possible battery SOC limits, the solution can be achieved with dynamic programming. This starts from SOC(end) and stepping backward in time. This way in every time step the optimal fuel use until the end of drive cycle is

calculated. Finally, the minimum fuel path is selected as an optimal solution.

)(endPd

In every step k the possible battery SOC range has to be considered and compared with the next range (step k+1) calculated in the previous step. For every SOC value in range k all possible SOC trajectories to range k+1 have to be calculated (limited with maximum battery charge and discharge). This is illustrated schematically in figure 5.

Figure 5. Sketch of dynamical programming solution

After determining the possible charge and discharge range (considering the limits), it can calculated the ICE fuel consumption for every trajectory from step k to k+1. Adding these fuel consumptions to every total fuel consumption from step k+1 to end, there result the possible total fuel consumptions from k to end starting from SOC(k). The minimum of the total fuel consumptions give the global optimal trajectory from SOC(k) to SOC(end). In step k these are calculated and stored for every possible SOC(k) values. After completing this procedure, in SOC(0) step, the global optimal total fuel consumption results. The optimal SOC trajectory can be determined following the minimum fuel path from SOC(0) to SOC(end). This results in the optimal sequence in time. bnPThis optimal input sequence can than be applied to the Simulink model of the vehicle. Test results are given in the following subsection.

2.2 CALCULATION AND TEST RESULTS FROM DYNAMIC PROGRAMMING

Calculations were performed for NEDC and FUDS drive cycles, considering the whole range of sun insolation on 25°C cell temperature. Reference results, without controller (but with battery charge with regenerative braking) were generated in (Bauer et al. 2006). They are summarized in table 1:

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0SOC 0.7192 0.7189 0.7186 0.7183 0.7181 0.7178

total fuel [g] 913.7265 916.015 918.1686 920.4583 922.613 924.768NEDC

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0SOC 0.7125 0.7122 0.7119 0.7116 0.7113 0.711

total fuel [g] 499.696 502.8127 505.7325 509.5911 513.0373 515.9575FUDS

Table 1. Reference results without controller


The dynamic programming gave less total fuel consumption in every case. Optimal SOC trajectory, fuel consumption and sequences are presented in figure 6, 7 and 8 for NEDC drive cycle, 1 kW/m

bnP

2 insolation and 25°C cell temperature. The SOC trajectory lies between the limits in every time step, moreover, it is near the desired value (0.7) during the entire time range. In fuel consumption (figure 7) horizontal sections mean that the ICE was turned off and no fuel consumption occurred during that time range. This is the case of regenerative braking or low power need satisfied from PV power. In sequence regenerative braking is strongly used to improve fuel economy.

bnP

Figure 6. SOC trajectory from NEDC drive cycle

Figure 7. Fuel consumption from NEDC drive cycle

Figure 8. Optimal sequence from NEDC drive

cycle bnP

Results from dynamic programming are summarized in table 2, while results from MATLAB Simulink vehicle model simulations with optimal sequence are summarized in table 3 (about the vehicle modelling, details can be found in (Bauer et al. 2006)).

bnP

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0

total fuel [g] 811.6438 814.3697 817.516 820.4546 822.6674 835.5047fuel spare [%] 11.172 11.096 10.96 10.865 10.8329 9.6525

NEDCλ [kW/m2] 1 0.8 0.6 0.4 0.2 0


FUDS

Table 2. Results from dynamic programming

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0SOC 0.7004 0.7005 0.7005 0.7005 0.7004 0.7004


NEDCλ [kW/m2] 1 0.8 0.6 0.4 0.2 0

SOC 0.7009 0.7008 0.7009 0.701 0.7009 0.7008total fuel [g] 421.7183 426.8949 434.7972 440.241 442.2611 444.2961

fuel spare [%] 15.605 15.098 14.026 13.609 13.796 13.889FUDS

Table 3. Results from simulations with optimal input sequence

bnP

As it is presented in table 2, DP results are almost the same for different insolation values, calculating with the same drive cycle. In the case of NEDC, the fuel spare ranges from 9.7 to 11.2 %, while in the case of FUDS it ranges from 23.13 to 26.15 %. This is mainly because NEDC needs higher drive power, which means more intensive battery use and constrained alternator usability for battery charge. Battery SOC is originally sustained by DP calculations. Table 3 shows that in the case of system model simulation with optimal input sequence lower fuel spare values can be achieved. This is due to continuous dynamics of the battery, in spite of moving between discrete battery charge level values as it was in the DP solution. However, overall charge sustainability requirement is satisfied in each case (see Table 3, SOC values).

bnP

Finally it is worth noting that, these results were calculated without limitation in changes of battery, EG and ICE power. So, sudden changes were allowed, as can be seen in figure 8. In real applications, of course, the limitation of battery power, EG power and ICE power derivatives have to be considered. This is the objective of future research and will decrease the fuel economy of the vehicle, but it is required for control strategy feasibility.

3. MODEL PREDICTIVE CONTROL FOR FUEL

CONSUMPTION MINIMIZATION

The second control strategy that was applied for the series HSV architecture is Model Predictive Control (MPC), as used also for a hybrid vehicle in (Back et al., 2002). MPC is an advanced control strategy which had spread significantly during the past years in industry as


well, due to its increasing popularity (Camacho and Bordons, 1999). The main advantages of MPC is that the basic formulation is extended to MIMO plants with almost no modification, on the other hand the basic concept of MPC is relatively easy to understand, and it is a powerful tool to cope with constraints effectively (Maciejowski, 2002). Without getting into a detailed presentation of MPC algorithms, the basic “elements” that build the problem formulation are the following: • Cost function that penalizes the deviations of the

predicted outputs from the reference trajectories; • Internal model of the plant; • Reference trajectory for the desired closed-loop

trajectory; • Possibility of defining constraints; • On-line optimization to determine the future

control strategy; • Receding horizon principle. For design and simulation of the fuel consumption minimization for a series HSV, the MPC Toolbox of Matlab is used. In this sense, the problem formulation follows the steps and form required by this design tool, based on the above presented elements. The first element to be defined is the plant model that is used in the predictive controller. This model is presented in detail in (Bauer et al., 2006), based on a detailed presentation of the components and their models. As it can be noted from (Bauer et al., 2006), the model is non-linear, so in order to apply the MPC tools a linearization is needed prior to it. This is achieved through the Matlab function linmod2, which creates a linear model from the non-linear system using an advanced method. The advantage is that the state variables of the system remain the original ones, so the physical meaning of the chosen state variables is kept. According to this, the states, inputs and outputs of the linearized plant are:

State variables: - x1: ICE power state, - x2: SOC, - x3: EM power state;

Inputs: - u1: ICE power, - u2: Battery nominal power; Controlled outputs: - o1: Drive power,

- o2: SOC, - o3: Fuel rate;

Measured disturbance input: - dm: PV panel power.

The PV power is considered as a measured disturbance (since it depends on the actual insolation which is an external factor that cannot be influenced) and treated as such, both in the modelling phase and in the controller design phase (Kulcsar and Bokor, 2006), (Maciejowski, 2002). For a SISO case, the basic idea for designing an application for the MPC Toolbox is depicted in figure 9, based on (Bemporad et.al., 2006).

PlantControllerMPC

Figure 9. Bloc diagram of a SISO MPC

Toolbox Application The numerical values for the linearized and sampled state-space model are (sampling time of Ts=0.001 sec. was chosen).

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡ ⋅+

+⎥⎦

⎤⎢⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⋅⋅−

⋅⋅+

+⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+++

−

−

−

−−

)k(x)k(x)k(x

1000001000800

)k(y)k(y)k(y

)3()k(d00

10321.6

)k(u)k(u

010638.210517.10

10321.61078.3

)k(x)k(x)k(x

9048.000010003679.0

)1k(x)1k(x)1k(x

3

2

1

3

2

1

m

4

2

1

7

11

46

3

2

1

3

2

1

The system is both observable and controllable, so MPC can be applied without problems. The acting constraints that are defined for the problem are the following:

⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

≤≤≤≤

≤≤−≤≤−

≤≤

3.7y08.0y6.0

58000y4000014000u26000

93000u0

3

2

1

2

1

(4)

The next step is the definition of the cost function that is used for the optimization. The aim is the fuel consumption minimization for the series HSV. A quadratic cost function is assumed that has the following form:

∑

∑

=

=

+∆

++−+=

uN

iiR

N

NiiQ

kiku

kikrkikykJ

0)(

2

)(2

)(ˆ

)()(ˆ)(2

1 (5)

Where )(ˆ kiky + are the predictions, at time k, of the output y, )( kikr + is the reference trajectory vector,

)(ˆ kiku +∆ are the changes of the future input vector (this term is necessary to ensure the reference tracking behaviour).


The tuning parameters of the cost function are as follows: • Prediction horizon: 10N,1N 21 ==

• Control horizon: Nu=4 • Penalties:

⎥⎦

⎤⎢⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=−

−−

15

154

100010

R,01.0000100000010

Q

The tuning parameters can be modified to obtain different performances. After defining the required parameters, the problem setup can be transposed into the following Matlab design tool (GUI of the MPC Toolbox) (figure 10). With its help, the final adjustments and also parameter modifications for new setups can be easily performed.

Figure 10. GUI setup for the given problem

The first simulation was the application of the NEDC drive cycle, transposed into required reference of drive power for r1 which is presented in figure 4. Also, for the SOC the constant reference of r2=0.7 was held, the third reference was r3=0 (for fuel rate). The simulation results are depicted in figures 11 (reference tracking), figure 12 (SOC and total fuel) and figure 13 (control signals ICE power and battery nominal power). It can be seen that the reference tracking is ensured by the predictive controller. The fuel consumption is between the global optimum value and the value calculated without controller (see table 1.). The SOC ensures a lower final value compared to the DP. This can be taken into account at a later global evaluation. Secondly, a different standard drive cycle is applied, namely the FUDS, presented in figure 14, together with the system output. The tuning parameters of the controller are the same as in the NEDC case.

0 200 400 600 800 1000 1200-4

-3

-2

-1

0

1

2

3

4

5

6x 104 PD reference signal tracking

Time [sec]

Pd [W

]

OutputReference

Figure 11. NEDC reference tracking

0 200 400 600 800 1000 12000.64

0.66

0.68

0.7

0.72SOC

Time [sec]

SO

C

0 200 400 600 800 1000 12000

200

400

600

800

1000Total fuel

Time [sec]

mf [g

]

Figure 12. NEDC SOC and total fuel consumption

0 200 400 600 800 1000 1200-2

0

2

4

6

8x 104 ICE power

Time [sec]

PIC

E

0 200 400 600 800 1000 1200-3

-2

-1

0

1

2x 104 Battery nominal power

Time [sec]

Pbn

Figure 13. ICE power and nominal battery power

The same signals are plotted as in the NEDC case, for comparison, namely the SOC and total fuel consumption (figure 15) and ICE power plus battery nominal power (figure 16).


0 200 400 600 800 1000 1200-1

-0.5

0

0.5

1

1.5x 104 PD reference signal tracking

Time [sec]

Pd [W

]

OutputReference

Figure 14. FUDS reference tracking

0 200 400 600 800 1000 12000.68

0.69

0.7

0.71SOC

Time [sec]

SO

C

0 200 400 600 800 1000 1200-200

0

200

400

600Total fuel

Time [sec]

mf [g

]

Figure 15. FUDS SOC and total fuel consumption

0 200 400 600 800 1000 12000

0.5

1

1.5

2x 104 ICE power

Time [sec]

PIC

E

0 200 400 600 800 1000 1200-2

-1

0

1

2x 104 Battery nominal power

Time [sec]

Pbn

Figure 16. FUDS ICE power and battery nominal

power It can be remarked that for the case when the FUDS drive cycle is used, the reference tracking is ensured acceptably well by the predictive controller. The fuel consumption is between the global optimum value and the value calculated without controller (see table 1.). The SOC ensures a lower final value compared to the DP.

7. CONCLUSIONS The paper presents two solutions for fuel consumption optimization of a series Hybrid Solar Vehicle (HSV). HSVs, having multiple main energy sources, are an alternative to conventional vehicles. Based on a brief description of the model of a series HSV, two control strategies are presented for fuel consumption optimization. The first control strategy is dynamic programming (DP) which is used to obtain a global optimum for fuel consumption. This is not an on-line solution, since it assumes that the future reference is entirely known. In the paper a DP solution was given, showing that the energy management concept is working for pre-defined drive-cycles. The second control algorithm is Model Predictive Control, implemented using the MPC Toolbox of Matlab. Simulations were performed for two drive cycles, namely for the New European Drive Cycle and for the Federal Urban Drive Schedule. In both cases the results are satisfactory, both concerning reference tracking and fuel consumption minimization. The fuel consumption lies between the global optimum values (calculated with DP) and values without controller. The results are very promising, still further research is needed to improve the methodology. The test simulations are performed for both strategies using Matlab/Simulink environment .

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the contribution of Hungarian National Science foundation (OTKA N:K060767). This work was partially supported by the Hungarian National Office for Research and Technology through the project "Advanced Vehicles and Vehicle Control Knowledge Center" (no: OMFB - 01418/2004).

REFERENCES I.Arsie, M.Graziosi, C.Pianese, G.Rizzo, M. Sorrentino

(2004). Optimization of Supervisory Control Strategy for Parallel Hybrid Vehicle with Provisional Load Estimate, AVEC ’04 (Department of Mechanical Engineering – University of Salerno).

M.Back, M. Simons, F. Kirschaum, V. Krebs (2002). Predictive Control of Drivetrains, IFAC 15th Triennial World Congress, Barcelona, Spain.

P.Bauer, Zs. Preitl, T. Peter, P. Gaspar, Z. Szabo, J. Bokor (2006). Control oriented modelling of a series hybrid solar vehicle, Workshop on Hybrid Solar Vehicles, November 6, 2006, University of Salerno, Italy.


A. Bemporad, M. Morari, N.L. Ricker (2006). Model Predictive Control Toolbox for Use with Matlab, Users’ guide, Version 2, The Mathworks Inc.

E.F. Camacho, C. Bordons (1999). Model Predictive Control, Springer Verlag London Ltd.

G.Gutmann (1999). Hybrid electric vehicles and electrochemical storage systems – a technology push – pull couple, Journal of Power Sources, Vol. 84, pp. 275-279.

M.W.T. Koot, J.T.B.A. Kessels, A.G. de Jager, W.P.M.H. Heemels, P.P.J. van den Bosch, M. Steinbuch (2005). Energy Management Strategies for Vehicular Electric Power Systems, IEEE Trans. on Vehicular Technology, 54(3), 771-782,.

B. Kulcsar, J. Bokor (2006). Measured Disturbance Estimation for Model Predictive Controller, Mediterranean Journal of Measurement and Control, Vol 2., No 3, July 2006.

S.E. Lyshevski (2000). Energy conversion and optimal energy management in diesel-electric drivetrains of hybrid-electric vehicles, Energy Conversion & Management, Vol. 41, pp. 13-24,.

J.M. Maciejowski (2002). Predictive Control with Constraints, Pearson Education Ltd.

G.Maggetto, J. van Mierlo (2001). Electric vehicles, hybrid electric vehicles and fuel cell electric vehicles: state of the art and perspectives, Ann. Chim. Sci. Mat, Vol. 26(4), pp. 9-26.

C. Musardo, G. Rizzoni, Y.Guezennec, B. Staccia (2005). A - ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management, European Journal of Control, 11 (4-5), pp. 509-524.

S. Piller, M. Perrin, A. Jossen (2001). Methods for state-of-charge determination and their applications, Journal of Power Sources, Vol. 96, pp. 113-120.


http://w3.bmt.tue.nl/nl/mensenbmt/?&script=showabstract.php&outid=5064


CONTROL ORIENTED MODELLING OF A SERIES HYBRID SOLAR VEHICLE

P. Bauer*, Zs. Preitl*, T. Péter*,P. Gáspár**,Z. Szabó**, J. Bokor**

* Budapest University of Technology and Economics, Dept. Of Transport Automation, H-1111 Budapest, Bertalan L.u. 2., Hungary

Email: [email protected], [email protected], [email protected] ** Computer and Automation Research Institute,

H-1518 Budapest, Kende u. 13-17, Hungary

Abstract: Nowadays more and more importance is dedicated to research in the field of alternative vehicles. An option to conventional vehicles, having usually as energy source a fuel tank with gasoline, consists in the so called hybrid electric vehicles (HEVs) which have multiple main energy sources. These energy sources are the conventional fuel tank and a battery, delivering both chemical and electrical energy. This can be completed with a photovoltaic (PV) panel resulting in a hybrid solar vehicle (HSV). HEVs and HSVs can be seen as a transition from conventional vehicles to fully electric ones. The paper presents a study on modelling a series HSV. The model can be used for the development of optimal control strategies which minimize the vehicle’s fuel consumption. After modelling all of the components of the HSV, two simulation structures were built in MATLAB Simulink. The first for basic simulations without control, the second for controller design for example with MPC Toolbox. The basic model is mainly a backward calculation scheme and provides reference solutions which can be compared with the controlled system behaviour. The control oriented model is a forward calculation scheme with given states, inputs and outputs. Linear models can be generated from it, were all states are controllable and observable. Keywords: hybrid solar vehicles (HSVs), component models, backward and forward calculations

1. INTRODUCTION The paper presents a study on modelling a series HSV. Series HSVs are optimal solutions for urban traffic applications where the vehicle starts and stops frequently during a drive cycle. So regenerative braking can be often used, which substantially improves the fuel economy of the vehicle. However, a series structure applies fully electric driving, where instantaneous large tractive forces provide good acceleration for the vehicle. The overall structure of series architecture is presented in figure 1. The vehicle model can be used for the development of optimal control strategies which minimize the vehicle’s fuel consumption. Finally, two types of models were generated. The first model, which is meant for basic calculations, provides reference data about the vehicle without controller. In this model, one can consider that regenerative braking only charges the battery, other control actions were not applied. The simulation scheme is mainly a backward calculation which determines the inputs from the required system outputs.

It can also be used for control action design with dynamical programming.

Figure 1. Series hybrid architecture

The second model that can be used for controller design, uses forward calculation scheme with given states, inputs and outputs. Controllers can be designed using this scheme, for example using the MPC Toolbox of Matlab. In the second section the specifications of all components of the series hybrid driveline are given.


The third section deals with MATLAB Simulink model construction and basic vehicle simulations. So reference data was generated about the HSV. Finally the conclusions end this paper.

2. COMPONENT MODELLING IN A SERIES HYBRID ARCHITECTURE

The architecture of a series hybrid vehicle can be seen in figure 1. First, the basic dynamics of the vehicle have to be considered, using different drive cycles. This way the extreme values of required drive power, torque and angular velocity can be calculated. After these calculations, the proper driveline elements can be chosen which fit the requirements. These elements are the following: The main part is the electric motor (EM) which drives the wheels or works as a generator during regenerative braking. The electrical energy for the EM is delivered by the electric generator (EG), the photovoltaic (PV) panel and battery. The electric generator is in rigid connection with the internal combustion engine (ICE). These two components have to be considered as an integral part of the vehicle, so power range, working points and efficiencies must be fitted. The internal combustion engine can be a diesel or a gasoline engine. The EM considered in such applications is usually a brushless DC motor which can be used both in motor and generator modes. PV panels can be used mainly during parking of the vehicle, but on open area, they are useful supplements for the electric power sources (EG and Battery) in driving too. The vehicle management unit (VMU) is used for control and coordination of the components. When designing the control strategies, one must consider the properties of all the components and the goals of the control application. Usually the main goals are minimum fuel consumption during a trip and battery charge sustaining. In the following subsections the modelling of each is component is presented in detail.

2.1 VEHICLE USED FOR HSV DEVELOPMENT

As a base vehicle, we selected the Porter glass van (see figure 2) used at the University of Salerno. Few technical data about the vehicle can be found in (Porter 2005-2006), but it is not enough even for basic dynamical calculations. So, one has to search for data about a similar van. This was the Subaru Libero mini van (Subaru 2006). Using the data about both vehicles, the parameters of the vehicle model are following:

o m=1400kg vehicle mass o Ad=2.724 m2 frontal area o Cd=0.6 air drag coefficient o Cr=0.015 rolling resistance coefficient o ρ=1.225 kg/m3 air density o wr=0.3m wheel radius o fr=4 final drive ratio o Battery voltage: 84V 6 x 14V cells o Battery capacity: 180Ah

Figure 2. Porter glass van (Porter 2005-2006, Micro-

Vett SPA)

For component selection, one has to calculate the power, torque and angular velocity requirements for the EM. This can be achieved using different drive cycles and the well known basic dynamical relations in the motion of vehicle. These relations are as follows:

2

( ) ( )

( ) ( )

1( ) ( )2

r

r

rd d

r

d d d

ft v t

ww

M t F tf

rF t m v v t A C m g C

ω

ρ

=

=

= ⋅ + ⋅ ⋅ + ⋅ ⋅&

(1)

Where ω is the angular velocity and Md is the torque required from the EM. The velocity v(t) is given in the specified drive cycles (for example figure 14, 15) and the acceleration ( ) can be simply calculated from it. So the required values for a given vehicle and drive cycle can be estimated. The considered drive cycles are: ECE_15, NEDC (New European Driving Cycle), FUDS (Federal Urban Driving Schedule), FHDS (Federal Highway Driving Schedule). The calculated maximal power, torque and angular velocity requirements are summarized in table 1.

( )v t&

Drive cycle Pmax [W] Mdmax [Nm] ωmax [rad/s]

ECE_15 15120 118.2 185.2NEDC 57089 234.52 444.45FUDS 10334 179.25 209.7FHDS 35075 162.3 357.375

Table 1. Power, torque and angular velocity requirements

As it can be observed, the EM must be able to deliver at least 57089W maximum power. So, the choice of an EM with 58 kW maximum mechanical power is suitable for this vehicle. Of course the ICE and EG must be fitted for this EM. This aspect will be discussed later in subsections dealing with ICE and EG.


2.2 ELECTRIC MOTOR (EM)

Usually an attractive alternative for electric vehicles and HEV driving systems are Brushless DC machines (BLDC-m) (Crowder, 1998), (Ehsani et al, 2001). They can function both in motor and generator regimes. As a remark to the BLDCs, it can be mentioned that the BLDC is in fact the combination of a permanently excited synchronous motor and a frequency inverter, where the inverter „replaces” the converter of a classical DC motor (Rizzoni, 1993), (Filippa et al, 2004). From here results also the name Brushless DC motor. BLDCs with inverter are mainly used in high performance electric drives with variable speed, where these values largely outrun the nominal rotation velocity. The BLDC-m is with “rare earth” magnetic materials (Samarium-Cobalt (Sm-Co) or other materials), which combine high flux-density with very large coercive force. The BLDC-m has its own electro-mechanical characteristics, it can not be used without a dedicated power supply unit and control system, consisting in: the power electronics unit: DC-AC or DC-AC - AC-DC (inverter), the command and the control unit (digital control unit), the BLDC-m servo-unit (Bay et al., 1996). A suitable solution consists in using DC-AC (AC-DC – for regenerative braking) inverter supply which ensures the torque control with injected current (PWM modulated control). The four-quadrant operation mode for the BLDC-machine with control block is presented below in figure 3, based on (Tsai, 2002).

Figure 3. Operation modes for a BLDC-m

In the paper the aspects regarding BLDC-m modelling refer to a qualitative modelling (machine plus power electronics structure) (Tsai, 2002), details regarding the pure machine are not presented. The qualitative modelling is achieved through the presentation of static characteristics, with two possibilities: • Steady-state torque-speed curves,

);( parameterUMf −=ω . The characteristics are based on relation:

)( 0IIKM t −= and =>

=>

nII ⋅≈ 1.00

IKM t9.0= MK

MK

Itt

1.19.01

=⋅

= (2)

]1,1[1 MK

RUK t

me

−=ω

Where M is the torque, I is current, U is voltage, Kt, Ke, are the electromechanical and the electromagnetic constants of the machine (their values are numerically close). • Steady-state speed-torque curves

);( parameterUfM −= ω ; they are obtained by inversing relation:

][1.1

ωem

t KUR

KM −= (3)

The characteristic steady-state curves for this latter case are presented in figure 4 (in normalised values). The diagram is presented in normalized values of the torque and speed, for the first quadrant according to figure 3. nn is the nominal resolution, in Pel=Pmax =constant regime.

Figure 4. Torque-speed characteristics in normalized

values

0 500 1000 1500 2000 2500 3000 3500 4000 4500

-200

-150

-100

-50

0

50

100

150

200

Speed (rpm)

Torq

ue (N

m)

Brushless DC motor drive and brake characteristics

Figure 5. Speed-torque characteristics for quadrants I

and II. For the given numerical data (nn=2300 rpm nominal RPM, Pn=58kW nominal mechanical power, Un=84V armature voltage, 80.η = efficiency factor) the speed-torque characteristics are given in figure 5, for different values of the armature voltage. It must be mentioned that the axes is figure 4 and figure 5 are inverted to the axes of figure 3. The maximum torque is obtained at the nominal armature voltage. The characteristics are presented for quadrants I and II, according to figure 5. Also the power balance between the electrical and mechanical powers is taken into consideration,


according to which Pel=Pm/η. The Simulink model of the BLDC-m is based on the above presented values.

2.3 PHOTOVOLTAIC (PV) PANEL

The PV panel is independent from the other components. It can be chosen so that it has maximum efficiency and a maintenance free robust structure. These requirements are all fulfilled with a crystalline, silicon on glass (CSG) 100 solar module manufactured by CSG Solar AG. Characteristics for the module are provided by the manufacturer in (CSG 2005) (see figure 6). In (Ocran et al., 2005) one can find detailed calculation formulas about PV panels, but lack of detailed data makes not possible to perform calculations with these formulas. So, finally exponential functions were fitted on the characteristics considering their exponential like shape (see figure 6). The form of the fitted function is as follows:

max

0 1U U

TUI K e−⎛ ⎞

⎜ ⎟= −⎜ ⎟⎜ ⎟⎝ ⎠

(4)

Where 0I is the output current, U is the output voltage, is the maximum possible output voltage, K and

are parameters to be calculated. maxU

UT

Figure 6. PV panel characteristics from (CSG 2005)

Calculations were performed for every insolation value (λ = 200÷1000 W/m2), so K and are insolation dependent. is also insolation dependent, so finally one can get the model fitting curves on K, and using insolation as independent variable. For

and third order polynomials were used while K could be approximated with a single linear function.

UT

maxU

UT

maxU

maxU UT

Another important aspect is the consideration of temperature effects in the model. This can be done using the temperature coefficient of power PK (CSG 2005). With this, the PV panel output power should be corrected. In (Ocran et al., 2005) a maximum power point tracker controller for PV modules is derived, so one can assume that the PV module is operated always in the maximum efficiency region. This results in a working line considering insolation as independent

variable. The U value at maximum power point ( ) is different for different insolation values, but a second degree polynomial describes it accurately.

optU

The final model for optimal PV panel power is as follows:

))25(1(

1)()( )()(max)(

−+

⋅⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−⋅⋅

=−

TK

eKU

P

P

UTUoptU

opt

PV

λ

λλ

λλ (5)

Equation (5) describes correctly the PV panel power at different insolation values, in maximum efficiency point with temperature correction (T is the actual cell temperature).

2.4 BATTERY MODEL For battery modelling both simple and complicated solutions can be found in the literature. One should select the proper battery considering the modelling purposes. We have selected a relatively complex one, which models the battery as a real voltage generator considering the change in open circuit voltage when battery state of charge (SOC) changes. The sketch of this model is presented in figure 7.

Figure 7. Battery model as real voltage generator

The governing equations of this battery model are as follows:

maxmax

int

int2

minmaxmin

)(2)(4

)(

QI

QQ

dtdSOC

RRPRRUU

I

SOCUUUU

b

t

btOCOCb

OCOCOCoc

==

+⋅

⋅+⋅−−−=

⋅−+=

&

(6)

In this type of formulation positive (battery power) means battery discharge, while negative means battery charge.

bP

bP

In (Koot et al., 2005) the efficiency of battery is also dealt with, which is modelled with the following expression:

5

5

103

10611−

−

⋅

⋅⋅−−= bn

bP

P (7)


Here means nominal battery power. The overall structure of the battery model, is presented in figure 8.

bnP

Figure 8. Battery simulation structure

The resultant battery model reflects all the important characteristics of a battery. The open circuit voltage decreases, when SOC decreases, the battery current calculation in (6) is asymmetric, which means that higher SOC rate can occur in discharging than in charging. Nominal power ( ) losses occur even in charging or discharging mode.

bnP

2.5 ELECTRIC GENERATOR AND INTERNAL

COBUSTION ENGINE MODEL

The electric generator and internal combustion engine (ICE) must be fitted to the electric motor and to each other. The selected electric motor with 58 kW maximum output mechanical power, needs maximum 72.5 kW input electrical power. This must be provided by the electric generator if battery discharge is not possible and the weather is cloudy (no insolation on PV panel). So, one has to select an electric generator that satisfies these requirements. Of course, the EG and ICE have to be fitted to each other using the maximum efficiency region for both of them. This way the EG can be described by a single characteristic curve, between input mechanical and output electrical power as in figure 9.

Figure 9. Electrical generator characteristic curve

The description of ICE is possible in a similar way considering the maximum efficiency working line. The fuel map of the proper ICE (which can satisfy the EG input power needs) is depicted in figure 10.

Figure 10. ICE fuel map

In the fuel map, the fuel rate values are plotted against ICE torque and angular velocity values. Every combination of torque and angular velocity means a possible output power value for the motor. However, fuel rate is given at every point, from which input power can be calculated using the lower heat value of gasoline.

The quotient of output and input power is the ICE efficiency. This way the efficiency map can be plotted against torque and angular velocity values (see figure 11.). Of course, in points with zero input and output power efficiency can not be calculated so one can simply assume it to be zero.

Figure 11. ICE efficiency map

The determination of optimal working line is possible using a characteristic value mixed from output power and efficiency:

ηω ⋅⋅= Mopt (8) The goal is to find the trajectory which contains the maximum power points from zero, to maximum possible output power, with maximum efficiency. For this purpose the map of opt values can be used (see figure 12.)


Figure 12. Optimum variable map for ICE with optimal

working line

The optimal working line can be found with a gradient method, starting from the point (M = 0, ω = 0). Further, the next paragraph deals with MATLAB Simulink model construction using the component models. 3 MATLAB SIMULINK MODEL CONSTRUCTION

Model construction has multiple goals. The first goal is to create a model for simulation without controller, which gives an insight into the original characteristics of HSV. The second goal is model construction for controller design. Of course, the resultant model will be strongly nonlinear, so the linearization of model is required or nonlinear control techniques must be used. The model for initial vehicle simulations (backward calculations) can be seen in figure 13.

Figure 13. Structure for basic HSV simulations

In this model, one has to apply only a very simple control decision, which covers battery charging with regenerative braking. Tests were performed for the NEDC (figure 14) and FUDS (figure 15) driving cycles, since these are the basic cycles used in urban traffic simulations. During calculations, the total fuel consumption and final battery SOC were registered. Of course, the

battery SOC has to increase because of regenerative braking and the lack of battery discharge. The initial SOC value is 0.7 according to the literature (Musardo et al., 2005, Koot et al, 2005).

Figure 14. New European Driving Cycle with time [s]

on horizontal and velocity [km/h] on vertical axis

Figure 15. Federal Urban Driving Schedule with time [s] on horizontal and velocity [km/h] on vertical axis

Simulations were performed for different insolation values. The resulting total fuel consumption data can be used as a reference for controller design, from which lower total consumptions have to be obtained. The results are summarized in table 2.

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0SOC 0.7192 0.7189 0.7186 0.7183 0.7181 0.7178

total fuel [g] 913.7265 916.015 918.1686 920.4583 922.613 924.768NEDC

λ [kW/m2] 1 0.8 0.6 0.4 0.2 0SOC 0.7125 0.7122 0.7119 0.7116 0.7113 0.711

total fuel [g] 499.696 502.8127 505.7325 509.5911 513.0373 515.9575FUDS

Table 2. Results from initial vehicle simulations

As it can be seen in table 2, the total fuel consumptions increase, while the final SOC values decrease at lower insolation values. The total fuel and SOC trajectories for both drive cycles at maximum insolation are in figures 16-19. As a conclusion from these figures, one can state that FUDS does not contain sudden high changes in parameters, while the final part of NEDC contains strong changes. This results in strong changes in total fuel and SOC. The cause of this is the extra urban part of NEDC with a maximum speed of 120 km/h.


Figure 16. Total fuel consumption trajectory, NEDC

Figure 17. Total fuel consumption trajectory, FUDS

Figure 18. SOC trajectory NEDC

Figure 19. SOC trajectory FUDS

This initial model can be a basis for optimal control input calculation with dynamic programming, while a

slightly different model should be constructed for other control design methods. For MPC control framework a forward calculation scheme is needed which can also be constructed from the component models. The selected model states, (control) inputs and outputs are:

State variables: - x1: ICE power state, - x2: SOC, - x3: EM power state;

Inputs: - u1: ICE power, - u2: Battery nominal power; Controlled outputs: - o1: Drive power,

- o2: SOC, - o3: Fuel rate;

Measured disturbance input: - dm: PV panel power.

The model can be linearized with MATLAB linmod or linmod2 functions. We have tested the resultant linear models and they were all controllable and observable so controller design for the HSV van is possible.

4. CONCLUSIONS

In this paper the control oriented modelling of components of a hybrid solar vehicle (HSV) and the overall vehicle structure was discussed. Components are mainly modelled with their characteristics (EM, EG, ICE), with calculation formulas (vehicle dynamics and battery) or with formulas derived from the characteristics (PV panel). After component modelling the construction of two different simulation structures in MATLAB Simulink was performed. The first model is for basic simulations and dynamic programming controller design, so it uses mainly backward calculation schemes. Only regenerative breaking is considered in it. The second model uses forward calculation which is proper for controller design in MPC framework. In this model the states, inputs and outputs are exactly defined. Simulations were performed only for the first model, generating reference total fuel consumption values for controller design. Of course, one has to get lower total fuel consumption from the controlled system. Results are summarized in table 2 for NEDC and FUDS drive cycles at several insolation values.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the contribution of Hungarian National Science foundation (OTKA N: K060767). This work was partially supported by the Hungarian National Office for Research and Technology through the project "Advanced Vehicles and Vehicle Control Knowledge Center" (no: OMFB - 01418/2004).


REFERENCES I.Arsie, M.Graziosi, C.Pianese, G.Rizzo, M. Sorrentino

(2004). Optimization of Supervisory Control Strategy for Parallel Hybrid Vehicle with Provisional Load Estimate, AVEC ’04 (Department of Mechanical Engineering – University of Salerno)

Osvaldo Barbarisi, Francesco Vasca, Luigi Glielmo (2006). State of charge Kalman filter estimator for automotive batteries, Control Enginnering Practice Vol. 14, pp. 264-275. (Science Direct)

O.F. Bay, G. Bal, S. Demirbas (1996). Fuzzy Logic Based Control of a Brushless DC Servo Motor Drive, Proc. Of the 7th International Power Electronics & Motion Control Conference Exhibition, Budapest, Hungary, vol. 3, pp.448-452.

R.M. Crowder (1998). Electric Drives and their Controls, Oxford University Press Inc., New York.

CSG 100, Crystalline Silicon on Glass (CSG) Solar Module, CSG Solar AG, Sonnenalle 1-5 06766 Thalheim, Germany, October 2005.

M. Ehsani, K.M. Rahman, M.D. Bellar, A.J. Severinsky (2001). Evaluation of Soft Switching for EV and HEV Motor Drives, IEEE Transactions on Industrial Electronics, Vol. 48, No.1, February 2001, pp.82-90.

Levent U. Gökdere, Khalid Belnyazid, Roger A. Dougal, Enrico Santi, Charles W. Brice (2002). A virtual prototype for a hybrid electric vehicle, Mechatronics, Vol. 12, pp. 575-593. (Science Direct).

G.Gutmann (1999). Hybrid electric vehicles and electrochemical storage systems – a technology push – pull couple, Journal of Power Sources, Vol. 84, pp. 275-279, 1999.

M.W.T. Koot, J.T.B.A. Kessels, A.G. de Jager, W.P.M.H. Heemels, P.P.J. van den Bosch, M. Steinbuch (2005). Energy Management Strategies for Vehicular Electric Power Systems, IEEE Trans. on Vehicular Technology, 54(3), 771-782.

C. Mi, M. Filippa, J. Shen, N. Natarajan (2004). Modeling and Control of a Variable-Speed Constant Frequency Synchronous Generator With Brushless Exciter, IEEE Transactions on Industry Applications, Vol.40, No.2, March/April 2004.

G. Maggetto, J. van Mierlo (2001). Electric vehicles, hybrid electric vehicles and fuel cell electric vehicles: state of the art and perspectives, Ann. Chim. Sci. Mat, Vol. 26(4), pp. 9-26.

C. Musardo, G. Rizzoni, Y.Guezennec, B. Staccia (2005). A – ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management, European Journal of Control, 11 (4-5), pp. 509-524.

Theodore Amissah Ocran, Junyi Cao, Binggang Cao, Xinghua Sun (2005). Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle, Tsinghua Science and Technology, ISSN 1007-0214 12/23, pp. 204-208, Vol. 10, Nr. 2., April 2005.

S. Piller, M. Perrin, A. Jossen (2001). Methods for state-of-charge determination and their applications, Journal of Power Sources, Vol. 96, pp. 113-120.

Porter glass van, technical data, Micro-Vett SPA, 2005-2006.

Peter Pudney, Phil Howlett (2002). Critical Speed Control of a Solar Car, Optimization and Enginnering, Vol. 3. pp. 97-107. (Science Direct)

G.Rizzoni (1993). Principles and Applications of Electrical Engineering, Richard D. Irwin Inc.

Andreas Schell, Huei Peng, Doanh Tran, Euthie Stamos, Chan-Chiao Lin, Min Joong Kim (2005). Modelling and control strategy development for fuel cell electric vehicles, Annual Reviews in Control, Vol. 29, pp. 159-168. (Science Direct)

Subaru Libero MY 91-96, www.subaruklub.hu, 2006. T-C. Tsai, M-C. Tsai (2002). Power Control of a

Brushless Permanent Magnet Electric Machine for Exercise Bikes, IFAC 15th Triennial World Congress, Barcelona, Spain, electronic format.

Mark Verbrugge, Edward Tate (2004). Adaptive state of charge algorithm for nickel metal hydride including hysteresis phenomena, Journal of Power Sources, Vol. 126, pp. 236-249. (Science Direct)




http://www.subaruklub.hu/

SIMULATION PROGRAM AND CONTROLLER DEVELOPMENT FOR A 4WD PARALLEL HEV

Ali Boyalıa, Murat Demircia, Tankut Acarmanb, Levent Güvença,* Burak Kırayc, Murat Yıldırımc

a Istanbul Technical University, Department of Mechanical Engineering, Automotive Control and MechatronicsResearch Center and MEKAR Laboratories

İnönü Cad. No:87 Gümüşsuyu, Taksim, TR-34437 İstanbul, Turkey b Galatasaray University, Faculty of Engineering and Technology, Computer Eng. Dept.,

Çırağan Cad. No:36, TR-34357 Ortaköy, İstanbul, Turkey c Ford Otosan, İzmit Gölcük Yolu 14. Km, TR-41680 Gölcük, Kocaeli, Turkey

Abstract: In this paper, we present a simulation model and a rule based controller design for a 4WD parallel HEV. A light commercial vehicle, equipped with inherited internal combustion engine, assembled with a battery pack, electrical actuator and its power converter is simulated by using the validated test results. A rule based controller and logic design is optimized to reduce fuel consumption and undesired emission with the assistance of the electrical actuator. Regenerative braking is shown to be capable of gaining back a certain percentage of the tire kinetic energy. The performance of the designed controller and logic switching between the two actuators are validated by experimental results. Copyright © 2006 IFAC Keywords: Control, modelling, design, rule-based systems, energy management systems

* Corresponding author, Prof.Dr. Levent Güvenç E-mail addresses : [email protected] URL : http://mekar.itu.edu.tr

1. INTRODUCTION Mass production of Hybrid Electric Vehicles (HEV) is becoming a global strategy for car manufacturers due to the prominent role of HEV in bringing down fossil fuel consumption and emissions. Hybrid vehicles are a temporary solution on the way to the zero emission road vehicle. Toyota is planning to produce all its vehicles with hybrid technology by 2012 (see Anonymous-a), and the sales volume of hybrid electric vehicles in the U.S. is expected to increase by 268 percent between the years 2005 and 2012 (see Anonymous-b). The effectiveness of fuel consumption depends not only on vehicle design but also on the control strategy used. Several HEV control strategies have been proposed in the open literature. The underlying

methodology in HEV control is to find the optimum power split ratio between the two power sources. The simplest and easiest to adapt control method is the rule based control algorithm (see for ex. Boyalı, et al, 2006). In this algorithm, the vehicle states are detected and the control commands are generated based on rules corresponding to the particular state. Rules are constructed based on engineering intuition and rigorous analyses of fuel consumption and emission maps belonging to the internal combustion engine (ICE), rather than analytical computation of optimum operating points based on minimization of a cost function. In some HEV applications, deterministic optimal control is applied, (see Lin, et al., 2003). For a given speed profile, the global optimum operation paths of vehicle components may be calculated using the dynamic programming method. However, in real-time driving conditions,


the speed profile is not known a priori and a global minimum can not be determined. The remedy is to find sub-optimal solutions approaching the global optimum. One of these suboptimal methods is to compute equivalent fuel consumption and to evaluate power split ratio instantaneously to minimize a chosen cost function (Sciarretta, et al., 2004; Paganelli, et al., 2001a; Paganelli, et al., 2001b; Johnson, et al., 2000). Another approach is to apply stochastic optimal control methods in the short time intervals while predicting the speed profile of the controlled HEV (Jeon, et al., 2001). This paper discusses the modeling and control of a four wheel drive hybrid electric vehicle and experimental test results. An explanation of the simulation model structure is given in section II. In sections III, the control algorithm involving vehicle states, transition states and switching logic between two actuators are explained. In Section IV, the hardware setup integrated into the experimental vehicle for performing the proposed control algorithm on a real-time basis is presented. Simulation results are demonstrated in section V. Experimental results are given in section VI. The paper ends with conclusions.

2. VEHICLE MODEL In this study, a four wheel drive Ford Transit commercial van is modeled using the Matlab/Simulink toolbox. Since rear and front wheel drive vans were commercially available, the experimental vehicle was formed by combining these two drive axles in one vehicle. The result was a four wheel drive (4WD) hybrid electric vehicle. The front drive is powered by the internal combustion engine and the rear drive is powered by the electric motor. A first prototype HEV of this construction was explained in our previous work in Boyalı, et al, 2006. This paper concentrates on a second prototype vehicle based on this 4WD concept, referred to as the experimental vehicle hereafter. Modeling of this experimental vehicle is presented first. The equations of dynamics for the considered model may be found in Boyalı, et al, 2006. The Simulink implementation of the model is shown in Fig. 1.

Fig. 1. Simulink vehicle model

This model consists mainly of six blocks. These blocks are the longitudinal vehicle model, nonlinear tire model, internal combustion engine model, electric motor (EM) model, driver model and supervisory controller. The net longitudinal force acting on the vehicle is used to compute vehicle acceleration by subtracting the resistance forces such as aerodynamic, rolling resistance and the resistance induced by road slope, from the traction forces that are available from the tire blocks. The Pajecka 2002 tire equations are used for modeling the tire. Although the tire model is capable of computing all tire forces and moments, only longitudinal forces are utilized in this model. The lateral forces and moments can be used for further studies such as hybrid vehicle lateral stability analysis due to the fact that the established model is modular in structure. The engine is modeled using engine maps that give the output engine torque for the two inputs of engine speed and accelerator pedal position. Transient regimes of the engine are thus not treated. Negative engine torque is computed to introduce function of cylinder head temperature and instantaneous engine speed. Transmission components are assumed to be rigid bodies, only equivalent inertias and transmission ratios are used to model the driveline. Even though the efficiency of transmission components varies with respect to transmission speed, gear ratio and the torque, constant efficiency values are used for computational simplicity. For a given speed profile, the driver model accepts the desired speed and actual speed as its two inputs. Anti-windup Proportional-Integral (PI) controllers are used to model the driver and to command the ICE and EM. Two feedback options are available. Speed feedback is not suitable for controlling the 4WD vehicle since the rear and front axle dynamics require different torques due to the different component properties. Thus, torque feedback is used to follow the desired speed profile. Once the desired speed starts to increase, the controller sends the throttle signal to the engine. Additionally, the driver model generates clutch and brake signals. To imitate the real clutch-engine relation for the EM only state, and to improve driving feeling while shifting gears with respect to conventional ICE vans, a potentiometer that generates a linear signal between “0” and “1” is used in the experimental vehicle. Look-up tables including data of braking torque versus brake pedal position are used for modeling the brakes. In order not to change braking characteristics of the vehicle, a force gap is allocated for regenerative braking. Along this gap, only regenerative braking is allowed. In designing regenerative braking, the regulations on braking are also taken into account. After a certain amount of applied pedal force, conventional friction brakes are


activated and the regenerative braking torque is decreased gradually as illustrated in Fig. 2. A simple equivalent circuit is used as the battery model. The open circuit voltage and internal resistance depending on state of charge and current flow direction are used to build the necessary equations. For simplification of the overall electric traction system modeling, a permanent magnet direct current motor model is used (see Boyalı, et al, 2006).

Fig. 2. Regenerative braking characteristics

3. RULES AND FINE TUNING

The main aim of introducing rule based control is to operate the ICE at high loads which correspond to its efficient regions. For this reason, the electric motor (EM) only mode operates under a predetermined driver power request and also when direct EM assistance is desired by the driver during gas pedal kick-down. The required power to drive the vehicle is computed for a given drive cycle. In real-time driving conditions, driver power or torque request at the wheels is computed by evaluating the accelerator pedal position and brake pedal force reading. Measured values are used in the ICE torque and brake maps and corresponding positive or negative

desired torques are calculated. There are five main vehicle states in the control algorithm which are, see (Fig. 3). • Standstill vehicle position (Standstill mode) • Pure EM excitation (EM mode)

• Pure ICE excitation (ICE mode) • Charging or EM assist (Hybrid mode) • Braking mode (regenerative and conventional

friction braking) To decide which state will be active, some transition rules are used. If the vehicle speed is below a small value such as 5 km/h, the vehicle is assumed to be in standstill position. Other state transitions are determined according to the logic rules given in Table I. To avoid limit cycle oscillations, hysteresis is added to the transitions.

Fig. 3. Vehicle states Traction torque is supplied by the EM in the pure EM mode where the ICE follows the wheel speed. Since the manual clutch can not be commanded automatically, the engine compression brake becomes active as shown in Fig. 4. This is an inherited disadvantage of the experimental vehicle towards HEV real-time operation as the EM should meet both the driver request and engine compression brake during the EM only mode. This drawback is

compensated since the engine cuts off fuel while braking. Another difficulty is to keep drivability of the hybrid electric vehicle at the same level as the conventional vehicle in the presence of a manual clutch. This can

Table 1. Transition Logic

Vehicle Speed

State of Charge

Requested Power. Max. ICE Torque Max. EM Torque Brake Pedal

Force Standstill <5 km/h -- -- -- -- --

Pure EM -- > SOClow < 6 kW -- < Requested. Torque --

Pure ICE -- < SOClow < 6 kW -- -- -- Pure ICE -- > SOClow > 7 kW > Requested. Torque -- -- EM Assist -- > SOClow -- < Requested. Torque -- --

EM Generator -- < SOClow -- <Req. Torque +Charge Torque < Charge. Torque --

Regen. Braking -- < SOChigh -- -- -- < 80

Conv. Braking -- >= SOChigh -- -- -- --

Conv. Braking -- < SOChigh -- -- -- > 90


be compensated by using appropriate transition functions between pure ICE and pure EM states and by using the clutch potentiometer to sense clutch position.

Fig. 4. Engine torque map The transition function is a function of the torque supplied by the power source at the wheels and time. If the transition conditions are realized between ICE and EM, the vehicle enters into the transition states (Fig 5.).

Fig. 5. Transition states During the transition states, the instantaneous required torque at the wheels is supplied by both power sources. For instance the EM power starts to decrease linearly as the ICE power increases linearly to keep on supplying the required power (Fig. 6.).

Fig. 6. EM and ICE torques in transition states Since the total torque always equals the demanded

torque, the driver does not feel an abrupt transition. The change is smooth and is not noticed by the driver. To avoid unwanted oscillations such as shunt and shuffle during the transitions, the demanded torque, engine torque and EM torque at the wheels are computed as accurately as possible. This is obviously an open loop control approach which uses available offline data. If an accurate engine map, i.e., torque output versus ICE speed, is available, an inverse map can be used to distribute required torque between the EM and the ICE. Another easier approach is to calibrate the accelerator pedal position in such a way that the EM generates the same amount of torque as the ICE for the same pedal position (Boyalı, et al, 2006). The current transmission stick shift position also has to be estimated in real time in order to compute the torque demand at the wheels. Vehicle speed and wheel angular speeds are available on the CAN bus. The ratio of these two speeds gives the transmission gear ratio and thus the stick shift position. There are upper and lower variations for each gear ratio as plotted in Fig. 7. The gear position estimation is carried out using a Stateflow diagram in Simulink.

Fig. 7. Gear ratio variations

4. HARDWARE SETUP

A dSpace MicroAutoBox (MABX) complemented with a RapidPro system is used as the main electronic control unit to carry out the HEV control algorithm. The MABX and Rapidpro system installed in the Ford Transit van is shown in Fig. 8.

Fig. 8. HEV controller hardware connections in the experimental vehicle


All signals required by the HEV controller are gathered via the MABX and the RapidPro signal conditioning units. Vehicle and battery states are monitored via reading the CAN bus. The other signals are analog signals. The general signal connection diagram is shown in Fig. 9. The HEV control strategy is modeled in Matlab/Simulink. Automatic code generation and downloading into MABX is achieved by the Matlab Real Time Workshop and dSpace Real Time Interface tools as illustrated in Fig. 10.

Fig. 9. General signal connection diagram

Fig. 10. Rapid HEV control algorithm prototyping

process diagram Following the electrical and mechanical flows plotted in Fig. 11, the EM driver enables the conversion of DC voltage to AC voltage. The electric power is supplied by a battery pack which is connected to the motor driver through a circuit breaker as a safety switch. The available EM driver control signals (enable, direction, acceleration,

brake) allow smooth operation of the EM via its driver. The HEV control unit sends the commands to the controller as acceleration or brake requests. The EM driver applies these requests according to the motor operating region or generator operating region maps.

Fig. 11. EM electrical and mechanical connections

(Boyalı, et al, 2006). 5. SIMULATION RESULTS WITH POWER-

ORIENTED CONTROL RULES

The EUDC drive cycle is used in simulation to compute fuel consumption and emitted emission quantities. The results are listed in Table II for a vehicle mass of 3000 kg. Emission values given in Table II are the engine-out emissions. SOC is short for state of charge of the batteries

Table 2. Fuel Consumption and Emissions

Conven. Hybrid Improv. Fuel Consp. Litre/100 km 11 9.3 % 15.5

Δ SOC % -- 0 --

NOx -- gr/km 0.77 0.55 % 28

CO2 -- gr/km 2.76 2.26 % 18

CO-- gr/km 5 4.75 % 5

Acceleration tests are also performed. For this reason, a gear shift algorithm pertaining to this vehicle is necessary. To determine the gear up shift points, the torque versus engine speed curves at the wheels were drawn for each gear (Fig. 12). The intersections of the curves are the gear shift points that maximize the area and thus acceleration performance under these curves. If this is repeated for each accelerator position with a specified increment, the gear shift graph in Fig. 13 is obtained. In hybrid acceleration tests, the EM operates in the assist mode according to the rule based control algorithm. As the pedal opening exceeds 70% of its full travel range, the EM starts to give assist torque linearly. Acceleration simulation results are given Table III and Figures 14-15.


Fig 12. Engine torque versus vehicle speed

Fig. 13. Optimal gear shift curves for acceleration

performance

Table III. Conventional and Hybrid Vehicle Acceleration Performances

Conventional [s] Hybrid [s] 8-32,3 km/h 2.086 2.08 8-56,4 km/h 5.6 5.6 0-100 km/h 22.37 17.13 80-120 km/h 18,76 12.34

Fig 14. Simulated Hybrid and Conventional Vehicle

acceleration performances

Fig 15. Simulated engine speed and gear position

history

6. EXPERIMENTAL RESULTS AND MODEL VERIFICATION

Accelerator, brake, clutch pedal and gear positions were recorded during an experimental acceleration test and were used as inputs to the simulation model in a subsequent simulation study. The experimental and simulation results are displayed in Figures 16 and 17. The simulated and real test results, with their close matching, show the effectiveness of the proposed simulation modelling approach. The HEV control algorithm states entered in the acceleration test are shown in Fig. 18.

Fig 16. Conventional vehicle acceleration

comparison of simulated and experimental responses

Fig 17. Hybrid vehicle acceleration comparison of

simulated and experimental responses


Fig 18. Vehicle speed and states during test drive

During driving tests, the state of charge of the

vehicle was also recorded and is shown in Fig. 19. In the charge state, the torque request of the driver is evaluated and a charge torque is calculated within component constraints. Since the ICE meets both the driver torque demand and the charge torque in the charge mode, the driver does not feel a significant change with respect to the conventional vehicle.

Fig 19. SOC change in Charge state

7. CONCLUSIONS

Two vehicles were successively converted into hybrid electric vehicles and instrumented with a battery, an electric motor and sensors. The second experimental vehicle is shown in Fig. 20. A simulation model and its use in designing a rule based control algorithm were presented. Simulation and experimental results were compared to demonstrate the validity of the results achieved. Future work will concentrate on the use of local and global optimization methods.

Fig. 20 Ford Transit Van and battery packs

ACKNOWLEDGEMENT The authors acknowledge the support of Ford Otosan R&D Department and the European Union Framework Programme 6 project INCO-16426.

REFERENCES Anonymous-a, http://www.automotivedigest.com. Anonymous-b, http://www.jdpower.com. Boyalı A., Demirci M., Acarman T., Güvenç L., Kiray B., Özatay

E. (2006), Modeling and Control of a Four Wheel Drive Parallel Hybrid Electric Vehicle, Proceedings of the IEEE Conference on Control Applications, Munich, Germany, November (to appear).

Lin C. C., Peng H., Grizzle J.W., and Kang J.M. (2003), Power Management Strategy for a Parallel Hybrid Electric Truck, IEEE Transaction on Control Systems Technology, Vol. 11, No. 6. pp 849-839,

Sciarretta A., Back M., and Guzzella L., Optimal Control of Parallel Hybrid Electric Vehicles (2004), IEEE Transactions on Control Systems Technology, Vol. 12, No:3. pp. 352-363.

Paganelli G., Ercole G., Brahma A., Guezennec Y., Rizzoni G. (2001), General Supervisory Control Policy for the Energy Optimization of Charge-Sustaining Hybrid Electric Vehicles, JSAE Review, Vol. 22, pp. 511–518

Paganelli G., Delprat S., Guerra T.M., Rimaux J., Santin J.J., (2001), Equivalent Consumption Minimization Strategy for Parallel Hybrid Powertrains, Proceedings of Vehicular Transportation Systems Conference, Atlantic City, NJ, USA.

Johnson V. H., Wipke K.B., and Rausen D.J. (2001), HEV Control Strategy for Real-Time Optimization of Fuel Economy and Emissions, SAE 2000-01-1543.

S. Jeon, K.B. Kim, S.T. Jo, and J.M. Lee (2001), Driving Simulation of a Parallel Hybrid Electric Vehicle Using Receding Horizon Control, Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on,

Vol. 2, pp. 1180-1185,


A MODEL FOR A HYBRID SOLAR VEHICLE PROTOTYPE

Ivan Arsie, Raffaele Di Martino, Gianfranco Rizzo, Marco Sorrentino

Department of Mechanical Engineering, University of Salerno, 84084 Fisciano (SA), Italy

Abstract: The paper deals with a dynamic model for the simulation of a solar hybrid prototype, developed in the framework of the Leonardo Program I05/B/P/PP-154181. The model is based on a longitudinal vehicle dynamic model and allows evaluating the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings, autonomy and costs. Simulation results show that significant fuel savings vs. conventional vehicle powered by internal combustion engine can be achieved for intermittent use in urban area and that economic feasibility could be achieved in the next future, considering the expected trends in costs and prices. Furthermore the hybrid series architecture allows increasing significantly vehicle autonomy vs. pure electrical vehicle. Keywords: modeling, simulation analysis, hybrid solar vehicles, photovoltaic energy, control.

1. INTRODUCTION

In the last years, increasing attention has been spent towards the applications of solar energy to cars. Various solar car prototypes have been built and tested, mainly for racing and demonstrative purposes [1]. Despite a significant technological effort and some spectacular outcomes, several limitations, such as low power density, unpredictable availability of solar source and energetic drawbacks, cause pure solar cars to be still far from practical feasibility. On the other hand, the concept of a hybrid electric car assisted by solar panels appears more realistic [3][4][5][6][7]. In fact, due to relevant research efforts [8], in the last decades Hybrid Electric Vehicles (HEV) have evolved to industrial maturity. These vehicles now represent a realistic solution to important issues, such as the reduction of gaseous pollution in urban drive as well as the energy saving requirements. Moreover, there is a large number of drivers utilizing daily their car, for short trips and with limited power demand. Some recent studies, conducted by the UK government, report that about 71 % of UK users reach their office by car, and 46 % of them have trips

shorter than 20 minutes, mostly with only one passenger (i.e. the driver) [9]. The above considerations open promising perspectives on the integration of solar panels with “pure”-electric hybrid vehicles (i.e. “tri-hybrid” cars), with particular interest in the opportunity of storing energy even during parking phases. In spite of their potential interest, solar hybrid cars have received relatively little attention in literature [7]. An innovative prototype has been developed at Western Washington University [5][6] in the 90s, adopting advanced solutions for materials, aerodynamic drag reduction and PV power maximization with peak power tracking. Other studies and prototypes on solar hybrid vehicles have been presented by Japanese researchers [3][4] and at the Queensland University [10]. Although these works demonstrate the general feasibility of such an idea, detailed presentation of results and performance, along with a systematic approach to solar hybrid vehicle design, seem still missing in literature. Therefore, appropriate methodologies are required to address both the rapid changes in the technological scenario and the increasing availability of innovative, more efficient components and solutions. A specific difficulty in


developing a Hybrid Solar Vehicle (HSV) model relates to the many mutual interactions between energy flows, power-train balance of plant and sizing, vehicle dimension, performance, weight and costs, whose connections are much more critical than in either conventional or hybrid electric vehicles. The current study focuses on the extension of the analysis methodologies presented in [11][12][18] to a hybrid solar vehicle prototype, now under development at DIMEC – University of Salerno. This activity is being conducted in the framework of the UE funded Leonardo project I05/B/P/PP-154181 “Energy Conversion Systems and Their Environmental Impact” [17]. The on going research is also extended to the study of real time control of solar panels (MPPT techniques and their implementation) and to the development of converters specifically suited for automotive applications [19].

2. THE SOLAR HYBRID VEHICLE MODEL

Different architectures can be applied to HEVs: series, parallel, and parallel-series. The two latter structures have been utilized for two of the more widely available hybrid cars in the market: Toyota Prius (parallel-series) and Honda Civic (parallel). Instead, for solar hybrid vehicles the series structure seems preferable [7], due to its simplicity, as in some recent prototypes of HSV [10]. With this approach, the Photovoltaic Panels (PV) assist the Electric Generator EG, powered by the Internal Combustion Engine (ICE), in recharging the Battery pack (B) in both parking mode and driving conditions, through the Electric Node (EN). The Electric Motor (EM) can either provide the mechanical power for the propulsion or restore part of the braking power during regenerative braking (Figure 1). In this structure, the thermal engine can work mostly at constant power (Pav), corresponding to its optimal efficiency, while the electric motor EM can reach a peak power PEM:

avEM PP ⋅= θ (1)

Figure 1 - Scheme of the series hybrid solar

vehicle.

2.1 Solar energy for vehicle propulsion In order to estimate the net solar energy captured by PV panels in real conditions (i.e. considering clouds,

rain etc.) and available for propulsion, a solar calculator developed at the US National Renewable Energy Lab has been used [12]. Four different US locations were considered, ranging from 21° to 61° of latitude, based on 1961-1990 time series. The calculator provides the net solar energy for different panel positions: with 1 or 2 axis tracking mechanism or for fixed panels, at various tilt and azimuth angles. The most obvious solution for solar cars is the location of panels on roof and bonnet, at almost horizontal position. Nevertheless, two additional options can be accounted for: (i) horizontal panels (on roof and bonnet) with one tracking axis, in order to maximize the energy captured during parking mode; (ii) panels located also on car sides and rear at almost vertical positions. The maximum panel area can be estimated as function of car dimensions and shape, by means of a simple geometrical model. An analysis of the effect of panel position at different latitudes has been presented recently by the authors [11]. The energy from PV panels can be obtained summing up the contribution from parking (p) and driving (d) periods. While in the former case it is reasonable to assume that the PV array has an unobstructed view of the sky, this hypothesis could fail in most driving conditions. Therefore, the energy captured during driving can be reduced by a factor β<1. In order to estimate the fraction of daily solar energy captured during driving hours (hd), it is assumed that the daily solar energy is distributed over hsun hours. A factor α<1 is then introduced to account for further degradation due to charge and discharge processes in the battery for energy taken during parking. The net solar energy available for propulsion, stored during both parking and driving modes, can therefore be expressed as:

αηsun

dsunsunPVpps h

hheAE −=, (2)

βηsun

dsunPVpds h

heAE =, (3)

Where esun is the average daily energy captured by solar panels in horizontal position. Hereinafter, esun is assumed equal to 4.3 kWh/day, which corresponds roughly to the year average at a latitude of 30°. The energy required to drive the vehicle during the day Ed (kWh) can be computed as function of the average positive power Pav (kW) and the driving hours hd:

( )∫ ⋅==hd

avdd PhdttPE (4)

The instantaneous power (P(t)) is estimated for assigned vehicle data and driving cycle, integrating a longitudinal vehicle model based on a dynamic vehicle simulator developed by the authors [15]. The model allows estimating the drive torque and power requested by the vehicle to accomplish the imposed driving cycle, depending on transmission ratio and

ICE

EG

B

PV

EM EN


efficiency, aerodynamic losses (CX, cross section) and weight. Thus, the required driving energy Ed depends on vehicle weight and aerodynamic parameters, which in turn depend on the sizing of the propulsion system components and on vehicle dimensions, related to solar panel area. Battery, electric motor and generator have been simulated by the ADVISOR model [16].

2.2 Vehicle weight

The parametric weight model of the HSV can be obtained adding the weight of the specific components (PV panels, battery pack, ICE, Generator, Electric Motor, Inverter) to the weight of the Conventional Vehicle (CV) equipped with ICE (WCV) and by subtracting the contribution of the components not present in the HSV (i.e. ICE, gearbox, clutch). Thus, the body (i.e. Wbody,HSV) and whole (WHSV) mass of the HSV can be expressed as:

( )gearICECVICECVHSVbody wwPWW +⋅−= ,, (5)

BuBPVPV

EMEMEGEGEG

ICEEG

HSVbodyHSV

NwwA

wPwPwP

WW

⋅++

+⋅+⋅+

+=

,

,

η (6)

Considering the lay-out described in Figure 1, the required nominal battery power is:

EGEMB PPP −= (7)

Therefore the number of battery modules is evaluated as:

uB

EGEMB P

PPN,

−= (8)

where PB,u is the nominal power of a single battery module. The power of the electric machine (PEM) is computed imposing that the HSV Power to Weight ratio (PtWHSV) equals the Power to Weight ratio of the reference vehicle:

CCbody

CCICEHSV W

PPtW

,

,= (9)

HSVHSVEM WPtWP ⋅= (10)

2.3 Cost estimation

In order to assess the benefits provided by HSV with respect to conventional vehicles, both the additional costs, due to hybridization and solar panels, and achievable fuel savings are to be estimated. The additional cost CHSV can be expressed starting from the estimated unit cost of each component:

ICECVICEBBEM

PVPVEGEGEG

ICEEGHSV

cPNCcP

cAcPcPC

⋅−++

+⋅+⋅=

,max

η (11)

The last term accounts for cost reduction for Internal Combustion Engine in HSV (where it is assumed PICE

= PEG/ηEG) with respect to conventional vehicle (where PICE = PICE,CV). The daily saving with respect to conventional vehicle can be computed starting from fuel saving and fuel unit cost:

( ) fHSVfCCf cmmS ⋅−= ,, (12)

The pay-back, in terms of years necessary to restore the additional costs with respect to the conventional vehicle, can be therefore estimated as:

SC

SnCPB HSV

D

HSV

300== (13)

For further details about the meaning and the values of some of the parameters introduced in eqs. 2 through 13, the reader is addressed to previous work [11] [18].

3. ENGINE CONTROL FOR HSV

In most electric hybrid vehicles, a charge sustaining strategy is adopted: at the end of a driving path, the battery state of charge should remain unchanged. With a solar hybrid vehicle, a different strategy should be adopted as battery is charged during parking hours as well. In this case, a different goal can be pursued, namely restoring the initial state of charge within the end of the day rather than after a single driving path [12] [18]. For this end, the internal combustion engine should be operated whenever possible at maximum efficiency, corresponding to power Popt. If the energy required to restore battery charge is lower than the amount corresponding to a continuous use at Popt throughout the driving time hd (case B), an intermittent operation can be adopted (cases A1-A2). In case that more energy is required, the internal combustion engine is operated at constant power between Popt and Pmax (case C). The different operating modes can be described by the variable φ, ranging from 0 to φmax = Pmax / Popt, as described in Tab. I. The optimal φ value is found by imposing that the energy provided by ICE and PV panels during the driving hours guarantees a charge sustaining strategy over the whole day. This condition is expressed as:

( ) ( )( ) 0

240

=∆+∆=

==∆ ∫

pd

hday

SOCSOC

dtdSOCSOC

φ

φφ (14)

Assuming that the driving schedule, of duration hd hours, is composed of a sequence of ECE-EUDC cycles, eq. (14) can be satisfied by iteratively solving, over one cycle, the following nonlinear equation:


( )cycles

pECE N

SOCSOC

∆−=∆ φ (15)

Tab. I – Engine control strategies for HSV.

A1 1<φ 0=ICEP dht φ<<0

A2 1<φ optICE PP = dd hth <<φ

B 1=φ optICE PP = dht <<0

C max1 φφ << optICE PP φ= dht <<0 where Ncycles is evaluated as function of each module duration Tcycle (h):

cycle

dcycles T

hN = (16)

The results obtained in previous papers show that relevant fuel savings, up to 45% for intermittent use in urban driving, can be obtained by a proper optimization of vehicle and powertrain components, and that this kind of vehicle is not far from economic feasibility, considering actual and expected trends in oil price and vehicle components (solar panels, batteries) [11][12][18].

4. RESULTS

The simulation results presented in this section are related to a prototype of solar hybrid vehicle with series structures that is being developed at the University of Salerno, within the EU supported Leonardo Program I05/B/P/PP-154181 “Energy Conversion Systems and Their Environmental Impact” (www.dimec.unisa.it/leonardo). The prototype is being developed starting from the Electric Vehicle Piaggio-Micro-Vett Porter (shown in Figure 2), whose main features concerning vehicle and electric motor are summarized in Tab. II. With the addition of solar panels and electric generator, whose details also are given in Tab. II, the HSV is obtained. Figure 3 shows the driving cycle selected for the simulation tests, which is derived from the European Driving Cycle (ECE) and is representative of a generic urban route. The power contributions of electric generator (EG), solar panels (PV) and battery (B) to drive the HSV along the imposed route is shown in Figure 4, while Figure 5 shows a comparison of SOC history between HSV, pure Electric Vehicle (EV) and solar electric vehicle (SEV), the latter been derived from the EV by the addition of solar panels to the base vehicle. Figure 4 evidences that since the variable φ is lower than 1, according to the imposed control strategy (Tab. I), the EG can be operated in an intermittent way at constant load and speed, corresponding to its highest efficiency (black line). Thus, in the former part of the transient, the drive power (blue line) is

exclusively supplied by the batteries (red line) that experience a decrease of State of Charge (SOC), as shown in Figure 5. This trend is inverted around 650 s when the EG is switched on and powers both vehicle and battery in order to meet the charge sustaining strategy (see Figure 5). Of course, due to the constraint introduced by eq. (15), the final SOC differs from the initial value by a fraction of the amount of energy provided by the PV panels during parking hours.

Figure 2 – The Micro-Vett Porter Electric Vehicle.

Tab. II – Electric Vehicle Technical Data.

Vehicle (EV, SEV, HEV)

Piaggio Micro-Vett Porter

Length 3.370 m Width 1.395 m Height 1.870 m Weight 1620 kg

Drive ratio 1:4.875 CX 0.4

Electric Motor(EV, SEV, HSV)

BRUSA MV 200 – 84 V

Max speed 52 Km/h Continuous Power 9 KW

Peak Power 15 KW Batteries

(EV, SEV, HSV) 14 Modules Pb-Gel

Mass 226 Kg Capacity 130 Ah

Photovoltaic Panels(SEV, HSV)

Polycrystalline

Surface 1.44 m2 Weight 60 kg

Efficiency 0.13 Electric Generator

(HSV) Lombardini (500 cc engine, 3 phase induction machine)

Max Power 15 kW Max Efficiency 25 % @ 9 KW

Weight 100 kg It is worth noting that the occurrence of an initial discharging process, followed by a recharging one,


results in benefits for batteries losses since the lower is the SOC, the more efficient is the recharging phase. On the other hand, the SOC trajectories simulated for EV and SEV (see Figure 5) both show a decreasing trend. This is expected in EV because battery recharge is performed by connection to the grid. For the SEV the same recharging strategy must be adopted since the amount of energy provided by the PV panels mounted on the roof is relatively small.

0 200 400 600 8000

5

10

15

20

25

30

35

Time [s]

Reference vehicle speed [km/h]

Figure 3 – Selected driving cycle.

0 200 400 600 800-10

-5

0

5

10

Time [s]

HSV power KW

drivegensunbatt

Figure 4 – Power contributions simulated for the

HSV over the selected driving cycle.

0 200 400 600 8000.725

0.73

0.735

0.74

0.745

0.75

0.755

0.76

Time [s]

SOC [/]

EVSEVHSV

cycles

p

NSOC∆

−

Figure 5 – Battery state of charge trajectories for

the three simulated vehicles.

Nevertheless, battery recharge in SEV is postponed with respect to EV due to the amount of energy provided by PV during parking hours. In the SEV

simulation this is taken into account by shifting up the initial SOC by a fraction of the energy stored during parking hours (Figure 5). This leads to a final SOC higher than the EV, which in turn results in increasing vehicle autonomy by about 30 % (125 against 95 km per battery cycle). Such a significant improvement indicates the use of PV panels as range extender of electric vehicles as a high potential application of solar energy in the transportation field.

2.4 Comparison with conventional vehicle equipped with ICE

The achievement of a charge sustaining strategy with the HSV suggests the need for assessing fuel economy improvements and economical aspects related to the solar hybridization of conventional cars. For this purpose, in this section a comparative analysis is performed on the HSV presented before and the ICE-powered Porter commercialized by Piaggio (equipped with an S.I. engine 1.2 liters with a max power of 48 KW; overall vehicle weight is 1550 kg). Figure 6 shows a comparison of engine speeds in case of hybrid and conventional vehicle, evidencing that in the latter case (solid line), the engine always operates in transient conditions and partial loads, with higher values of specific fuel consumption and poor efficiency, as evidenced in Figure 7. On the other hand, as already shown in Figure 4, the hybrid vehicle ICE generator works only in the latter part of the transient, operating at constant speed (i.e. 3000 rpm) corresponding to its maximum efficiency (i.e. 32%). The different behaviour of engine operation results in a significant improvement in fuel economy in case of HSV, as indicated in Tab. III. For the selected driving cycle, the amount of fuel needed by the hybrid vehicle is 50 % less than that required by the ICE-powered vehicle.

0 200 400 600 8000

500

1000

1500

2000

2500

3000

3500

Time [s]

rpm [rev/min]

HSVCV

Figure 6 – Comparison between HSV and CV

ICE’s rpm over the imposed driving cycle.

Tab. III also gives the pay-back in terms of years necessary to restore the additional costs of the HSV with respect to the conventional vehicle. With the actual costs of fuel and PV the pay-back equals 7.7 years, whereas assuming to double the fuel price and to reduce by 75 % the PV cost, the pay-back reduces considerably, down to 2.4 years.


It is worth mentioning here that other strategies are possible for HSV control, such as letting the ICE run during parking mode too: in that case, the engine can be used to restore battery charge by working always at its maximum efficiency.

0 200 400 600 8000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time [s]

ICE efficiency [/]

HSVCV

Figure 7 – Comparison between CV and HSV

ICE’s Efficiency over the imposed driving cycle.

Tab. III – Energetic and economical aspects associated with solar hybridization.

HSV CV

Fuel consumption (g per cycle) 79 158

Weight (kg) 1780 1550

Pay-back (years, with actual costs) 7.7 /

Pay-back (years, considering future cost trends)

2.4 /

Driving hours per day 2 2

Insolation (KWh/m2/day) 4.3017

5. CONCLUSION

A dynamic model for the simulation of a solar hybrid prototype based on the electrical vehicle Piaggio Micro-Vett Porter has been presented. The model describes the energy flows between photovoltaic panels, internal combustion engine (ICE), electric generator, electric motor and batteries, considering vehicle longitudinal dynamics and the effect of control strategies. Vehicle weight is computed starting from the electrical vehicle weight, considering the effects of additional components (ICE-generator, photovoltaic panels, etc.). The model also predicts the additional costs with respect to conventional vehicle and the pay-back.

The simulation performed along a urban driving cycle has shown that the hybrid vehicle can accomplish a charge sustaining strategy with intermittent use of ICE-generator at maximum efficiency. Comparison with conventional vehicle powered with ICE has evidenced a significant improvement in terms of fuel economy, close to 50 % in the selected driving cycle. Furthermore, the pay-back to restore the additional costs of hybrid components is 7.7 years with actual costs of fuel and components while it decreases to 2.4 years assuming to double the fuel price and to reduce the panels cost by 75%, in accordance with the actual and expected trends in costs and prices.

6. REFERENCES

[1] Hammad M., Khatib T. (1996), Energy Parameters of a Solar Car for Jordan, Energy Conversion Management, V.37, No.12.

[2] Wellington R.P. (1996), Model Solar Vehicles Provide Motivation for School Students, Solar Energy Vol.58, N.1-3.

[3] Saitoh, T.; Hisada, T.; Gomi, C.; Maeda, C. (1992), Improvement of urban air pollution via solar-assisted super energy efficient vehicle. 92 ASME JSES KSES Int Sol Energy Conf. Publ by ASME, New York, NY, USA.p 571-577.

[4] Sasaki K., Yokota M., Nagayoshi H., Kamisako K. (1997), Evaluation of an Electric Motor and Gasoline Engine Hybrid Car Using Solar Cells, Solar Energy Material and Solar Cells (47), 1997.

[5] Seal M.R. (1995), Viking 23 - zero emissions in the city, range and performance on the freeway. Northcon - Conference Record 1995. IEEE, RC-108.p 264-268.

[6] Seal M.R., Campbell G. (1995), Ground-up hybrid vehicle program at the vehicle research institute. Electric and Hybrid Vehicles - Implementation of Technology SAE Special Publications n 1105 1995.SAE, Warrendale, PA, USA.p 59-65.

[7] S.Letendre, R.Perez, Christy Herig, Vehicle Integrated PV: a Clean and Secure Fuel for Hybrid Electric Vehicles, Proc. of Annual Meeting of the American Solar Energy Society, June 21-26, 2003, Austin, TX.

[8] Arsie I., Graziosi M., Pianese C., Rizzo G., Sorrentino M. (2004), Optimization of Supervisory Control Strategy for Parallel Hybrid Vehicle with Provisional Load Estimate, Proc. of AVEC04, Arhnem (NL), Aug.23-27, 2004.

[9] Statistics for Road Transport, UK Government, http://www.statistics.gov.uk/CCI/nscl.asp?ID=8100.

[10] http://ww.itee.uq.edu.au/~serl/UltraCommuter.html.

[11] Arsie I., Marotta M., Pianese C., Rizzo G., Sorrentino M., Optimal Design of a Hybrid Electric Car with Solar Cells, Proc. of 1st AUTOCOM Workshop on Preventive and Active


Safety Systems for Road Vehicles, Istanbul, Sept.19-21, 2005.

[12] Arsie I., Rizzo G., Sorrentino M., Optimal Design and Dynamic Simulation of a Hybrid Solar Vehicle, SAE paper 2006-01-2997.

[13] Marion B. and Anderberg M., “PVWATTS – An online performance calculator for Grid-Connected PV Systems”, Proc.of the ASES Solar 2000 Conf., June 16-21, 2000, Madison, WI.

[14] http://www.autosteel.org/articles/2001_audi_a2.htm

[15] Arsie I., Flora R., Pianese C., Rizzo G., Serra G., A Computer Code for S.I. Engine Control and Powertrain Simulation. SAE 2000 Transactions - Journal of Engines, Vol. 109-3, SAE Paper 2000-01-0938, pp. 935-949.

[16] Burch, S., Cuddy, M., Johnson, V., Markel, T., Rausen, D., Sprik, S., and Wipke, K., 1999, "ADVISOR: Advanced Vehicle Simulator", available at: http://www.ctts.nrel.gov.

[17] Leonardo Program I05/B/P/PP-154181 “Energy Conversion Systems and Their Environmental Impact”, http://www.dimec.unisa.it/leonardo.

[18] Arsie I., Rizzo G., Sorrentino M., Optimal Design of a Hybrid Solar Vehicle, Proc. of AVEC’06, Taipei (TW), August 20-24, 2006.

[19] I.Arsie, M.Cacciato, A.Consoli, G.Petrone, G.Rizzo, M.Sorrentino, G.Spagnuolo, “Hybrid Vehicles and Solar Energy: a Possible Marriage?”, ICAT06, November 17, 2006, Istanbul.

7. CONTACT

Ivan Arsie ([email protected]) Raffaele Di Martino ([email protected]) Gianfranco Rizzo ([email protected]) Marco Sorrentino ([email protected]) Tel. +39 089 964080 – Fax +39 089 964037 Web www.dimec.unisa.it

8. DEFINITIONS, ACRONYMS, ABBREVIATIONS

Es,p: Solar energy stored during parking hours (kWh)

Es,d: Solar energy stored during driving hours (kWh)

ηp: PV efficiency

ΑPV: PV surface (m2)

wICE: ICE weight to power ratio (kg/kW)

wgear: Gearbox weight to power ratio (kg/kW)

wEM: Electric motor weight to power ratio (kg/kW)

wEG: Electric generator weight to power ratio (kg/kW)

wB,u: Single battery module weight (kg/kW)

wPV: PV specific weight (kg/m2)

PEG: Electric generator power for HSV

ηEG: Electric generator efficiency

cICE: ICE cost to power ratio (Eur/kW)

cEG: Electric generator cost to power ratio (Eur/kW)

cPV: PV specific cost (Eur/m2)

cEM: Electric motor cost to power ratio (Eur/kW)

cB: Single battery module cost (Eur)

cf: fuel unit cost (Eur/kg)

nD: number of days per year in the pay-back analysis

∆SOCday: state of charge variation over the whole day

∆SOCd: state of charge variation in driving phases

∆SOCp: state of charge variation in parking phases


A model of mismatched photovoltaic fields for simulating hybrid solar vehicles

G.Petrone*, G.Spagnuolo*, M.Vitelli°

*DIIIE, Università di Salerno Via Ponte Don Melillo, Fisciano (SA), Italy

[email protected], [email protected]

°DII, Seconda Università di Napoli Real Casa dell’Annunziata, Aversa (CE), Italy

[email protected] Abstract – A numerical model of photovoltaic fields that allows simulating both uniform and mismatched operating conditions is introduced in this paper. It allows the simulation of a photovoltaic generator whose subsections, e.g. cells, groups of cells, panels or group of panels, work under different solar irradiation values and/or different temperature. Furthermore, different nominal characteristics, rated power, production technology, shape and area can be accounted for any subsections of the photovoltaic generator. The proposed model is reliable and results into a non linear system of equations that requires a moderate computational burdensome, both in terms of memory use and processor speed. Numeric simulations confirm the usefulness of the proposed approach in automotive applications, especially in solar hybrid vehicles, in order to design a proper electronic controller ensuring the extraction of the maximum power from the photovoltaic generator.

I. INTRODUCTION Renewable energy sources are gaining more and more interest in recent years due to the exploitation of oilfields and to political crises in some strategic areas of the world. Among them, photovoltaic (PV) sources have found new applications, e.g. solar hybrid vehicles. They work with greatly varying solar irradiation levels due to the movement and, especially if the solar cells are not placed only on the roof, different subsections of the PV generator may receive different sun irradiance levels. In any case, it is mandatory to match the PV source with the load/battery in order to draw the maximum power at the current solar irradiance level. To this regard, a switching dc-dc converter controlled by means of a Maximum Power Point Tracking (MPPT) strategy is suitable to ensure the source-load matching by properly changing the operating voltage at the PV array terminals in function of the actual weather conditions. Any efficient MPPT technique must be able to detect the voltage value corresponding to the maximum power that can be delivered by the PV source. In literature, many MPPT strategies have been proposed, the greatest part of them being derived by the basic Perturb and Observe (P&O) and Incremental Conductance (IC) approaches. Both P&O and IC strategies, if properly designed, correctly work in presence of a uniform irradiance of the PV array, since they are able, although by means of different processes, to detect the unique peak of the power vs. voltage characteristic of the PV array. Unfortunately, in automotive applications, the PV field does not receive a

uniform irradiation and/or not all its parts (panels as well as single cells) work at the same temperature, so that mismatches among different parts of the array may arise. Such a situation has been evidenced in literature and the detrimental effect due to a panel of a PV array working under an irradiation level or at a temperature, which is sensibly different than that characterising the other panels has been experimentally investigated. Mismatching conditions are more likely to occur in automotive applications than in stationary ones. For example, parts of the array may be shaded by other parts of the vehicle when the sun is at low angle and, moreover, unpredictable shading takes place when the vehicle passes under the shadows of buildings, trees, advertising panels. Even in automotive applications characterized by a relatively small duty cycle in the use of the vehicle, mismatching may play a strong role on battery charging during the long parking time. In such cases the shadows produced by objects surroundings the car can give rise to a marked waste of available solar energy. To relieve the power drop caused by a mismatch, a bypass diode is used in anti-parallel with each PV basic unit, e.g. a panel. A blocking diode is placed in series with each totem of PV basic units connected in series. This precaution increases the plant cost, but avoids that a basic PV unit or a series of them absorbs the current produced by others. Whenever a mismatch occurs, both P&O and IC based MPPT techniques have a high probability to fail the MPPT goal. Indeed, the power vs. voltage characteristic of a PV field under a uniform solar irradiation exhibits a unique maximum point that is easily tracked by standard MPPT techniques. Unfortunately, mismatches deeply affect the shape of the PV characteristic, which may exhibit more than one peak, with one absolute maximum point and one or more relative points of maximum power. In this case, standard MPPT techniques are likely deceived and consequently track a point where dP/dv=0, but that is not the maximum power point. In order to design a MPPT strategy able to perform a “global” tracking of the true PV array voltage associated to the maximum power, without being trapped in local maxima, it is of fundamental importance the realization of an accurate numerical model of the PV field. It must be able to simulate the PV basic units mismatching in a reliable and fast manner,


also accounting for the behaviour of real bypass and blocking diodes. In this paper a model with these characteristics is introduced: features and drawbacks are illustrated by means of simulations carried out in Matlab and PSIM environments. The paper is organized as follows: Section II shows the details of the proposed model and puts in evidence its features. Section III shows the results of some application examples and Section IV is devoted to conclusions and hints for a future work.

II. THE MODEL

In fig.1 the usual circuit model of a photovoltaic (PV) panel is shown.

Fig.1 Circuit model of a PV panel including the bypass diode Db.

Such a model recurs in literature very often (e.g. in []). It includes the light induced current generator Iph and series and shunt resistances Rs and Rh respectively; Db is the bypass diode. We suppose, without loss of generality, that one bypass diode is placed in antiparallel with the whole panel. The relation between the PV generator current I and voltage V is evaluated by solving the following system of non linear equations:

−= 1eII d,t

d

VV

d,satd (1)

−=

−

1eII db,tVV

db,satdb (2)

hdphdb IIIII −−+= (3)

( )dbsssd IIRVIRVV −⋅+=⋅+= (4)

( )h

dbs

h

dh R

IIRVRVI −⋅+

== (5)

It has been obtained by using Kirchhoff voltage and current laws (3) and (4), linear characteristic equations for shunt and series resistors (4) and (5), and non linear equations for the diode D included in the model of the panel (1), and for the

bypass diode Db (2). In (1) Vt,d=ηd⋅VT,d and in (2) Vt,db=ηdb⋅VT,db, Vt,d and Vt,db are expressed as the product of the diode ideality factor and the thermal voltage. The latter, as well as the two saturation currents Isat,d and Isat,db, depend on temperature T only, whilst the light induced current Iph depends on the irradiance level S and on the array temperature T [1]. The system of equations (1)-(5) clearly shows that the PV array current I is a nonlinear and implicit function of the PV array voltage V, of the irradiance level S and of the temperature T. Nevertheless, such a non linear system can be symbolically solved in one of the symbolic calculation environments, such as Matlab and Mathematica, actually available. In this way, a non linear relationship between the current I and the voltage V at the basic PV unit terminals can be obtained. For space reasons such relationship is reported in (6), at the end of the paper. It makes use of the LambertW function of the term θ whose value depends on the terminal voltage V and is reported in (7). It is well known [3] that the LambertW function of the variable θ, herein indicated as LambertW(θ), is a non linear function of θ and it is the inverse function of: ( ) θ⋅θ=θ ef (8)

Note that the use of the LambertW function allows the apparently explicit calculation of the array current as a non linear function of the terminal voltage. The value of the Lambert function, for an assigned value of the independent variable θ, is efficiently provided in simulation environments such as Matlab and Mathematica. Expression (6), together with well known LambertW function properties, allow to calculate the first derivative of the panel’s current with respect to the terminal voltage, again in apparently explicit form. In (9) it has been reported the property expressing the derivative of the LambertW(θ) function with respect to θ, and in (10) the expression of the derivative of I with respect to V at the panel’s terminals is given (see the end of the paper). In this way, the differential conductance of the panel is explicitly expressed as function of the panel’s voltage V only, by means of a non linear function. Thus, in this way, both the PV current and its derivative with respect to the PV voltage have been expressed in closed form as functions of the sole voltage. This greatly helps in formalizing the non linear algebraic system that describes a PV field composed by an arbitrary number of panels ,which can be connected both in series and in parallel. In order to explain this concept, let us refer to a string of PV panels connected in series. Fig.2 shows the string of N series-connected panels and the blocking diode that avoids current backflows.


Fig.2 String of N PV panels connected in series and including the blocking

diode. In order to model this series, it is possible to build up a system of (N+1) equations in the same number of unknowns V1,V2,...,Vk,...,VN-1,VN,Vdiode. It is enough to write one Kirchhoff voltage law and N Kirchhoff current laws. The topological constraints are formalized in (11) at the end of the paper; they can be matched with the N equations of the panels, expressed as in (6) in terms of Ik=Ik(Vk), k=1,2,...,N, and with the characteristic equation of the blocking diode expressed in the form (1), and taking into account the dependency of such a characteristic equation from the physical parameters of the real diode used. The non linear system (11) includes N non linear equations and one linear equation, the first one, in which the terminal voltage V, that is assumed to be a known term, appears . Each non linear equation includes only two of the (N+1) unknowns, and the first one is always V1. This choice has been made to simplify the expression of the Jacobian matrix needed to solve the non linear system by means of, for example, the Newton Raphson method. Thanks to (10) it is possible to obtain each term of the Jacobian matrix J as a function of the unknowns. Moreover, the structure of the system has been properly chosen in order

to simplify the structure of the Jacobian matrix that, as it is well known, needs to be inverted when using Newton Raphson iterative methods. The Jacobian matrix structure is reported in (12) which puts in evidence that it is sparse and with a pattern which is characteristic of doubly bordered and diagonal square matrices [2]. Moreover, the first row is composed by (N+1) constants, while all the other rows require the evaluation of dI1/dV1 and the calculation of just another derivative. As a whole, the evaluation of the system (11) requires N times the use of the equation (6) and one time the (1); the calculation of the Jacobian matrix requires N evaluations of (10) and one evaluation of (13). Such features are useful both in terms of memory requirements during the simulation and of computation time. In Section III the features of the method are described by means of a numeric example.

III. SIMULATION RESULTS Simulations have been conducted by considering Kyocera KC120 PV panels, characterized by 36 series connected cells, each one of area 0.0225 m2, Rs=0.006 Ω, Rh=104 Ω. A string with two PV panels connected in series, and with the blocking diode has been simulated. In this case the order of the system is 3. The panels have been considered identical in terms of manufacturing parameters and working temperature (T=320K). On the other hand, their irradiation level has been considered very different, namely S=1000 W/m2 for the first panel and S=100 W/m2 for the second one. The whole simulation has been conducted in Matlab environment; it required 45.3 s (on an Intel Centrino 2.0 GHz platform) in order to calculate 100 linearly spaced points of the power-voltage characteristic of the PV array. The samples of the current in the series and of the voltage distribution over the three devices have been also stored during simulation. The curves are reported in figs.3 and 4. They put in evidence the effect of the panel that receives the lower irradiance level in terms of string current drop at high voltage values. It is worth noting that the curve of fig.3, obtained under mismatching conditions of the PV string, exhibits two maxima at two different voltage levels, with that one occurring at about 44 V being characterised by a consistently lower value of the power with respect to the other one placed at about 18 V. This occurrence can compromise the energy conversion operated by the switching converter connected at the string terminals and responsible for the MPPT. This can be understood by comparing plots of fig.3, representing the mismatched string, with that one of fig.5, obtained by imposing a unique irradiance level S=1000 W/m2 for both the panels. If the MPPT controller acts so that the string works at about 40 V under uniform irradiance, it ensures that the maximum power – about 260 W – is converted. If a sudden irradiance drop (from S=1000 W/m2 to S=100 W/m2) occurs on one panel and the MPPT algorithm is not able to perform a “global search” of the new maximum power point, the relative maximum placed at about 40 V (see fig.3) is the


likely new operating point. This means that the MPPT controller is not able to track the real maximum power point and that about 90 W (the difference between the maximum power of the best operating point at about 18 V and the power of an operating point placed at about 44 V) are wasted due to MPPT algorithm limit. Such considerations have been verified by means of a PSIM simulation of the PV field controlled by means of boost switching converter that performs the MPPT function and matches the PV field with a 48V battery (see fig.6). The layout puts in evidence two dynamic link libraries that implement the PV field (left) and the P&O based MPPT controller (bottom). It has been simulated a sun irradiance drop involving one of the two panels of the array: the steep transition between the characteristic of fig.5 and that one of fig.3 occurs at t=0.03s (see fig.7). The P&O controller tracks the lower maximum because the voltage at which it occurs (see fig.8) is close to the voltage corresponding to the unique maximum of the characteristic depicted in fig.5. Fig.7 also put in evidence the three-points behaviour at both steady states: this characterizes the hill climbing of the two maximum power points tracked at the two different conditions. This result is confirmed by the boost converter duty cycle variation shown in fig.9. In conclusion, the model illustrated in this paper might be of great help in developing an improved MPPT algorithm that is robust with respect to this kind of conditions, since it allows to test the MPPT performances with respect to different shapes of the power-voltage characteristic of the PV generator.

IV. CONCLUSIONS AND FUTURE WORK

In this paper a non linear model of mismatched photovoltaic fields is introduced. It allows to simulate heterogeneous arrays, with subsections (cells, groups of cells, panels or groups of panels) characterized by different irradiation levels, temperatures, semiconductor materials, areas, operating parameters and so on. The model also allows to take into account manufacturing tolerances and drifts ascribable to aging effects. Further work is in progress in order to use the simulator in order to develop and test a maximum power point tracking strategy able to ensure an efficient power conversion even if the photovoltaic field works in mismatched conditions.

REFERENCES

[1] S. Liu, R. A. Dougal: ”Dynamic multiphysics model for solar array”, IEEE Trans. On Energy Conversion, Vol. 17, No. 2, June 2002, pp. 285-294. [2] William H. Press, Numerical Recipes in C, The Art of Scientific Computing, Second Edition, Cambridge University Press, 2002. [3] http://mathworld.wolfram.com/LambertW-Function.html

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Fig 3. Power [W] vs. voltage [V] characteristic of the simulated mismatched

PV field.

-2 0 2 4 6 8 10 12 14 16 18 20 22 24 260

1

2

3

4

5

6

7

Fig.4 Current [A] vs. voltage [V] characteristic of the three devices in the simulated string. Continuous line = blocking diode curve, dashed line =

curve of the panel with irradiation S=100 W/m2, dash-dotted line = curve of the panel with irradiation S=1000 W/m2.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

Fig 5. Power vs. voltage characteristic of the simulated matched PV field.

W

W

V

V

A

V


Figure 7. PV field output power.

Figure 8. PV field voltage.

Figure 9. Duty cycle during transient.

Figure 6. PSIM layout for the simulation of the MPPT controller.

( )[ ]( ) ( )θ⋅−

−⋅+

+−+⋅

=−

LambertWRV

1eIRR

VIIRI

s

d,tVV

db,satsh

d,satphh db,t (6)


( )( )

( )

d,t

RRVVRIIRR

d,satsh

VeIR//R shd,t

hd,satphsh

+⋅

⋅++⋅⋅

⋅⋅=θ (7)

( ) ( )[ ] ( )( )( )[ ] θ⋅θ+θ

=⋅θ+

=θθ θ LambertW1

LambertWeLambertW1

1LambertWdd

LambertW (9)

( ) ( ) ( )θ⋅+⋅

−⋅−+

−=−

LambertWRRR

ReVI

RR1

dVdI

shs

hVV

db,t

db,sat

sh

db,t (10)

( ) ( )( ) ( )

( ) ( )

( ) ( )( ) ( )( ) ( )

=−=−

=−

=−

=−=−

=−+++++++

−−

−

0VIVI0VIVI

0VIVI

0VIVI

0VIVI0VIVI

0VVVVVVV

diodediode11

NN11

1N1N11

kk11

3311

2211

diodeN1Nk21

K

K

KK

(11)

∂∂

−∂∂

∂∂

−∂∂

∂∂

−∂∂

∂∂

−∂∂

∂∂

−∂∂

∂∂

−∂∂

=

−

−

diode

diode

1

1

N

N

1

1

1N

1N

1

1

k

k

1

1

3

3

1

1

2

2

1

1

VI

VI

VI

VI

VI0

VI

VI

VI

0VI

VI

VI

VI

111...1...111

JOM

OM

(12)

diode,t

diode

VV

diode,t

diode,sat

diode

diode eVI

VI

⋅−=∂∂

(13)


THE PROFITABLENESS OF HYBRID SOLAR VEHICLES (HSV)

Ion V. Ion, Ion C. Ionita, Daniela Negoita, Spiru Paraschiv

„Lower Danube” University of Galati – Romania Thermodynamics and Heat Engines Department

Abstract. Being conscious that nowadays in the starting stage the competition between classical car, powered by combustion engine and the HSV can live and develop only with an additional financial support, the authors focused their attention on mathematical expression of this support. They found the factors affecting the value of this support and the conditions making HSV profitable. The analysis is based on the compared cost to quality analysis, developed in the last 10 years. Keywords: Compared cost-to-quality analysis

List of the used symbols Latin letters:

ICEPC -the total cost of a classical car, powered by

internal combustion engine, [€ / ICE car] HSVPC -the total cost of a HSV, [€ / HSV] ICESC -the cost of the transport service in the case

ICE, [€/ km ICE] HSVSC -the cost of the transport service with HSV,

[€/ km HSV]

( )ICES I

C -the investment cost of the ICE transport

service, [€ / km ICE]

( )ICES C

C -the consumption cost of the ICE transport

service, [€ / km ICE]

( )ICES OM

C -the operation-maintenance cost of the

ICE transport service, [€ / km ICE]

( )HSVS I

C -the investment cost of the HSV transport

service, [€ / km HSV]

( )HSVS C

C -the consumption cost of the HSV

transport service, [€ / km HSV]

( )HSVS OM

C -the operation-maintenance cost of the HSV

transport service, [€ / km HSV]

( )ICEP I

C -the investment cost of the ICE car, [€/car ICE]

( )ICEP C

C -the consumption cost of the ICE car, [€/car

ICE]

( )ICEP OM

C -the operation-maintenance cost of the ICE car,

[€/car ICE]

( )HSVP I

C -the investment cost of the HSV, [€/ HSV]

( )HSVP C

C -the consumption cost of the HSV, [€ / HSV]

( )HSVP OM

C -the operation-maintenance cost of the HSV,

[€/ HSV] HSVOMs -the operation-maintenance ratio of HSV car service,

(eq. 8); ICEOMs -the operation-maintenance ratio of ICE car service,

(eq. 7) ICEf -the unitary fuel consumption of ICE, [l/100km ICE]

ICEfc -the unitary fuel cost, [€ / l fuel]


HSVfk -the fuel reduction ratio of HSV, (eq. 10); ICEOMp -the operation-maintenance ratio of ICE car

product, (eq. 11); HSVOMp -the operation-maintenance ratio of HSV

product, (eq. 12); 100 kmHSVS - the state unitary subsidy of HSV program,

[€/ 100 km] Greek letters:

ICEτ -the total life cycle of an ICE car, [km ICE / ICE car]

HSVτ -the total life cycle of a HSV, [km HSV / HSV car] Subscripts: I-investment C- consumption P – product S – service OM - operation-maintenance f – fuel HSV – hybrid solar vehicle Superscripts: ICE – internal combustion engine HSV – hybrid solar vehicle

1. INTRODUCTION

The purpose of this paper is to analyze mathematically the conditions when HSV could be profitable. Starting on this way, we know that presently the classical cars are cheaper than HSV. This reality can be changed not so late in the future because of some tendencies we see: 1) The classical cars pollution is increasing permanently, due to raising number of vehicles, in spite of their lowering individual pollution; 2) The solar cell panels are permanently perfectible and their efficacy is continuously increasing while their cost is lower and lower; 3) The unitary cost of organic fuel is presently increasing exponentially. Being conscious that nowadays in the starting stage the competition between classical car, powered by combustion engine and the HSV can live and develop only with an additional financial support, the authors focused their attention on mathematical expression of this support. They found the factors affecting the value of this support and the conditions making HSV profitable. The analysis is based on the compared cost-to- quality analysis, developed in the last 10 years [6 – 18]. To obtain the expression of the necessary subsidy, the authors considered two evident different cases: a) the case of a classical car, powered by combustion engine (symbols with superscript ICE); b) the case of a HSV powered both by combustion engine and by photo-voltaic (PV) panels (symbols with superscript HSV).

2. CHOOSING THE NECESSARY COST-TO QUALITY RATIO

As the compared cost- to- quality analysis needs, when starting the evaluation it is necessary to choose an adequate cost-to quality ratio. There are two possible variants: a. The production variant, where we have to calculate in

terms of Euro/car; b. The service variant, accountable in terms of Euro/100

covered kilometers. The authors considered the second variant option (b) to be more appropriate because it expresses better the service the car does, taking into consideration that the car is used more or less during its life cycle span.

3. THE QUALITY PARAMETERS OF THE CONSIDERED CARS

For each car, classical or hybrid one, there are 31 different quality parameters: QP 01–Accessibility; QP 02–Adaptability; QP 03–Availability; QP 04–Cleanliness; QP 05–Credibility; QP 06–Durability; QP 07–Environmental Protection; QP 08–Fuel Consumption; QP 09–Functional Engine Parameters; QP 10–Inflammability; QP 11–Lighting Parameters; QP 12–Look; QP 13–Maintainability; QP 14–Parking Capacity; QP 15–Productivity; QP 16–Promptitude; QP 17–Protection; QP 18-PV Panel Parameters; QP 19-Reliability; QP 20–Safety; QP 21–Size; QP 22–Style; QP 23–Susceptibility; QP 24–Pneumatic Tires Parameters; QP 25–Toxicity; QP 26–Transportability; QP 27–Transport Capacity; QP 28–Vulnerability; QP 29–Watching capacity; QP 30–Weight; QP 31–Workings. When considering the transport service made by these cars, we have at least another 15 parameters: QS 01–Accessibility; QS 02–Accuracy; QS 03–Comfort; QS 04–Competence; QS 05–Confidence; QS 06–Credibility; QS 07–Efficacy; QS 08–Efficiency; QS 09–Feedback speed; QS 10–Formalism; QS 11–Honesty; QS 12–Proficiency; QS 13–Promptitude; QS 14–Punctuality; QS 15–Safety.

4. THE COST EQUATION

The total cost of the purchased ICE car is:

( ) ( ) ( )OMICEPC

ICEPI

ICEP

ICEP CCCC ++= [€/ICE car] (1)

The total cost of the purchased HSV is:

( ) ( ) ( )HSV HSV HSV HSVP P P PI C OM

C C C C= + + [€/HSV car]

(2) The total cost of the ICE car transport service is:

( ) ( ) ( )OMICESC

ICESI

ICES

ICES CCCC ++= [€/km ICE] (3)

The total cost of the HSV transport service is:

( ) ( ) ( )OMHSVSC

HSVSI

HSVS

HSVS CCCC ++= [€/km HSV] (4)

Taking into consideration that: ( ) ICEICE

PIICES CC τ/= [€/km ICE] (5)


( ) HSVHSVPI

HSVS CC τ/= [€/km HSV] (6)

( ) I

ICES

ICEOMOM

ICES CsC )(= [€/km ICE] (7)

( ) I

HSVS

HSVOMOM

HSVS CsC )(= [€/km HSV] (8)

( ) 100ICE ICE ICES fC

C f c /= [€/ km ICE] (9)

( ) 100/ICE

fICEHSV

fCHSVS cfkC = [€/ km HSV] (10)

( ) IICEP

ICEOMOM

ICEP CpC )(= [€ / ICE car] (11)

( ) IHSVP

HSVOMOM

HSVP CpC )(= [€ / HSV] (12)

the expression of ICE transport cost becomes:

( ) ( ) +τ+++= ICEC

ICEPI

ICEP

ICEOM

ICEOM

ICES CCpsC /])()1[(1

100/ICEf

ICE cf+ [€/km ICE] (13) while that of HSV transport cost is:

( ) +++= IHSVP

HSVOM

HSVOM

HSVS CpsC )()1[(1

( ) 100//] ICEf

ICEHSVf

HSVC

HSVP cfkC ++ τ

[€/km HSV] (14)

5. THE STATE SUBSIDY Knowing that presently the ICE transport is cheaper than that of HSV:

HSVS

ICES CC < [€ / km] (15)

to encourage the development of HSV research and development it is necessary the subsidy km

HSVS , so that:

HSVS

kmHSV

ICES CSC =+ [€ / km] (16)

From equations (13), (14) and (16) we can obtain the expression of the necessary subsidy:

( ) ( )( )[( ) ] ( ) ( )( )[ +++−τ+

+++=

IICEp

ICEOM

ICEOM

HSVc

HSVp

IHSVp

HSVOM

HSVOM

kmHSV

Cps/C

CpsS

11

11

( )] ( ) 1001 /cfk/C ICEf

ICEHSVf

ICEc

ICEp −+τ+

[€/km] (17)

6. THE PROFITABLENESS OF HSV

The relation (17) is essential when analyzing the profitableness of HSV. It allows us to see the influence of the main factors, to find out how could we give up to subsidy km

HSVS , making the HSV profitable. For this, we have to consider

0=kmHSVS (18)

In this case ( ) ( )( ) ( )[ ]( ) ( )( ) ( )[ ] +τ+++=

=τ+++ICE

cICEpI

ICEp

ICEOM

ICEOM

HSVc

HSVpI

HSVp

HSVOM

HSVOM

CCps

CCps

/11

/11

( ) 100/1 ICEf

ICEHSVf cfk −+ [€ / km] (19)

7. MATHEMATICAL MODELING, RESULTS AND DISCUSSION

In the reference papers [1, 20] we found reasons to consider ICE

pHSVp CC 3.1= ; ( ) ( )

OMICEpOM

HSVp CC = ;

HSVICE τ=τ 8.0 ; 8.0...6.0=HSVfk ;

ICEfc 1.77...3.54= €/l fuel.

These data are argued below. From [20] we can read: “Hybrid vehicles do cost more than their gasoline-only counterparts. On average, the price premium is $2,500 to $3,000. Buyers, however, do have the benefit of a $2,000 federal tax deduction for purchasing a hybrid as part of the Internal Revenue Service's Clean Fuel Vehicle deduction. The deduction, which was put into place as an incentive for consumers to consider this new technology, was scheduled to decline gradually beginning in 2004 and eventually be phased out. Congress has extended this credit, however, offering up to a $2,000 tax credit on hybrids placed into service in 2004 and 2005. The credit drops to $500 for 2006. Boughey received the $2,000 federal deduction as well as a state deduction of $3,600, which was calculated based on his purchase of a hybrid as well as on the vehicle he replaced — a 1991 Mercury Grand Marquis that was sold for salvage. For comparison purposes, Laumann calculated first-year insurance costs for all the versions of the 2004 Honda Civic four-door sedan including the Civic Hybrid. Costs ranged from $835 to $849 for an average driver in the state of California with the Civic Hybrid falling near the middle at $844. Like the other automakers, Toyota has also done a lot of testing of its hybrid-specific components. Its battery packs in particular have lasted for over 180,000 miles in testing. "We've looked at all the things that put stress on batteries, such as the discharge/charge cycles and extreme temperatures," says Dave Hermance, executive engineer for environmental technology at Toyota. When it comes to regular maintenance, most hybrids do not require any maintenance on the hybrid-specific components. One notable exception is an air filter on the Ford Escape Hybrid. "The air filter for the battery system needs to be replaced every 40,000 miles," explained Olson. The gasoline engine in a hybrid requires the same maintenance that it would if it were the only power source in the vehicle. That means oil changes every 5,000 to 10,000 miles depending on the vehicle and the driving conditions. Another component that regularly needs to be replaced on every vehicle is the brake pads, but with hybrids these last much longer thanks to regenerative braking. In


regenerative braking, the electric motor becomes a generator and captures the energy that would be lost as heat through the brakes when the vehicle's brakes are applied or when it is coasting. Once the energy is captured, it is transformed into usable electricity, which recharges the batteries and in turn increases the number of miles than can be traveled per gallon of gasoline. An added benefit is that the reduced heat means less wear and tear on the brakes, which means that they don't need to be replaced as often as conventional brakes. "We've seen customers go 85,000 miles before they needed to replace their brakes on their Prius vehicles," says Toyota's Hermance. One of the top reasons that people purchase a hybrid vehicle is to get better fuel economy and they are often disappointed that they don't experience the fuel economy numbers listed on the window sticker in their regular driving. "I just love my Honda Civic Hybrid, but I have been a bit disappointed that the gas mileage isn't better," says Ivey Doyal of Atlanta, Ga. To be sure, differences in projected fuel economy versus real-world driving can mean serious differences in your wallet over the long term. Unfortunately, there is a discrepancy between the EPA's fuel economy ratings, which are listed on the window sticker when you buy a new car or truck, and the real-world results that most drivers experience, regardless of the type of vehicle they drive. The EPA's ratings are the numbers manufacturers are required by law to list in all the promotional materials for their vehicles. Unfortunately, the procedure the EPA uses to calculate these numbers is outdated and isn't indicative of the way most Americans drive today. The EPA has made adjustments to its calculations to try to compensate for this. Even with these adjustments, however, the numbers still often differ from the real world. "We've seen where the typical driving style can be as much as 20-percent less than the EPA fuel economy number," says Bienenfield. While all vehicles are affected by this discrepancy, hybrid vehicles have the appearance of being affected even more so. "For example," explains Bienenfield, "A vehicle that has a fuel economy rating of 20 mpg may only get 18 mpg, while a vehicle that is rated at 50 mpg may only get 45 mpg. This seems like a bigger issue for the more fuel-efficient vehicle, but in reality both vehicles are off by 10 percent." In the informal survey we did with Honda and Toyota hybrid owners, fuel economy numbers ranged from 33 to 49 mpg on average, which reflected many driving styles and a wide range of commutes. While these numbers are significantly lower than the EPA ratings, all the owners we interviewed were happy overall with the fuel economy as it is still better than most gasoline-only vehicles. Perhaps what is most misleading about the fuel economy ratings is that they don't show how widely numbers can vary based on an individual's typical driving route. "Short trips are the harshest on fuel economy, so anyone who drives just a few miles in his typical trip will see lower mpg numbers than

someone who drives, say, 15 miles to work," says Bienenfield. Our unscientific poll showed these results as well. Pittsburgh, Pa., resident Jen Bannan typically drives just a few miles in each trip and, as a result, had the lowest fuel economy of those we interviewed, averaging 33 mpg in her 2002 Toyota Prius. "Is (the lower fuel economy) disappointing? Sure, but I'm still filling up less than I did in my old car and the Prius is the best car I've ever owned," she said. At the opposite end of the spectrum, Civic Hybrid driver Boughey and Honda Insight owner Dana Dorrity of Tivoli, N.Y., have commutes of 60 and 50 miles one way, respectively, on roads with rolling hills. Both had the highest fuel economy of those we spoke with, at 47 mpg for Boughey and 49 mpg for Dorrity. Poughkeepsie, N.Y., resident Mary Koniz Arnold has no trouble averaging 50 mpg in her 2001 Toyota Prius (which she bought used in April 2004) on longer trips, but she averages closer to 40 mpg during her one-way commute of 10 miles. "To be fair," says Toyota's Hermance, "there is no way any two tests will give the range of consumer exposure in terms of driving conditions and temperatures. He continued, "We are really measuring the wrong thing. Since you don't get to choose how many miles you drive, we should be measuring the gallons consumed." Reading this large variety of documentary reasons, the reader can understand better how difficult was the authors’ task to collect numerical data for their study. Finally the authors made the following hypotheses:

07.0=HSVOMs ; 05.0=ICE

OMs ; 10000=ICEPC €;

40.0== ICEOM

HSVOM pp ; ( ) ( )

IICEpI

HSVp CC 2.1= ;

75000=τ=τ ICEHSV km ICE or HSV / ICE car or HSV; ( ) ( )

CICEpC

HSVp CC 2.1= ; 7=ICEf l / 100 km

ICE; ( ) 0 4ICE ICEp pI

C . C= ; ( ) 0 4ICE ICEp pC

C . C=

By using these data and the mathematical model previously presented, the functions ( )km HSV

HSVS τ (fig. 1),

( )km ICEHSV fS c (fig. 2), ( )km HSV

HSV fS k (fig. 3), were

calculated. From the fig. 1 we can see how the state unitary subsidy of HSV 100 km

HSVS [€ / 100 km] is influenced by total life cycle of a HSV, τHSV [km HSV/ HSV car]. The diagram was calculated with the values previously indicated and inserted in diagram field. The compared cost-to-quality analysis applied here shows us that: 1) The state unitary subsidy 100 km

HSVS [€ / 100 km] is lowering when the total life cycle of HSV τHSV [km HSV/ HSV car] is increasing. In other words, the more resistant in time is HSV, the less is the necessary unitary state subsidy. How much must be this total life cycle of HSV so that the state subsidy to not be necessary? The calculus results shows HSVτ = 830000 km for ICEτ = 75000 km and

HSVτ =101500 km when ICEτ = 93750 km. Of course, these results are unacceptable, we must have in view other practical solutions, like the fuel reduction ratio HSV

fk increasing or to manufacture cheaper the HSV (the value HSV

PC ).


2) The fig. 1 diagram shows also that the less is the total life cycle of the ICE cars (the value ICEτ ) the

unitary state subsidy 100 kmHSVS [€ / 100 km] is lower.

Fig. 1. The necessary subsidy 100 km

HSVS [€/100km] versus the total life cycle of HSV HSVτ [km HSV / HSV car].

Fig. 2. The necessary subsidy 100 kmHSVS [€/ 100 km] versus the fuel reduction ratio HSV

fk of HSV.

The total life cycle of a HSV, HSVτ [km HSV / HSV car]

7.5 8 8.5 9 9.5x 104

115

120

125

130

135

140

145

150

155Th

e st

ate

unita

ry su

bsid

y of

HSV

,

100 kmHSVS

, [€/

100k

m]

ICEτ =93750 km

ICEτ =75000 km

100 kmHSVS = 0 for HSVτ =83 104 km

100 kmHSVS = 0 for HSVτ =101.5 104 km

HSVPC =13000 €;

ICEPC = 10000 €;


OMs ; 40.0== ICEOM

HSVOM pp ;

( )HSVp I

C = 4800 €; ( )HSVp C

C = 4800 €;

( )ICEp I

C = 4000 €; ( )ICEp C

C = 4000 €;

ICEf = 7 l / 100 km; HSVfk = 0.8; ICE

fc = 1 € / l

0.5 0.55 0.6 0.65 0.7 0.75 0.80

0.5

1

1.5

2

ICEfc =2 € / l

The fuel reduction ratio of HSV, HSVfk

ICEfc =1 € / l

The

stat

e un

itary

subs

idy

of H

SV,

100 kmHSVS

, [€/

100k

m]

HSVPC =13000 €;

ICEPC = 10000 €;


OMs ; 40.0== ICEOM

HSVOM pp ;

( )HSVp I

C = 4800 €; ( )HSVp C

C = 4800 €; ( )ICEp I

C = 4000 €;

( )ICEp C

C = 4000 €; HSVfk =0.8

ICEf = 7 l / 100 km; ICEτ = HSVτ =75000 km


Fig. 2 gives some answers to the questions arisen when examining the fig. 1. 1). The first conclusion at glance is that with the gas price ICE

fc = 1 € / l, if we can obtain HSVfk = 0.57,

the HSV manufacturing and sale does not need state subsidy. 2). The second conclusion is that the state subsidy

100 kmHSVS increases when we use lesser the PV panels

(the value HSVfk is bigger).

3). The third conclusion is that when the gas price ICEfc increases, the state subsidy 100 km

HSVS is lowering, becoming even zero if the fuel reduction ratio of HSV, HSV

fk = 0,787 and this price reaches to ICEfc =2 € / l.

Fig. 3 is showing intuitional conclusions: 1) The necessary subsidy 100 km

HSVS , [€/100 km] decreases

when the unitary fuel cost ICEfc [€ / l fuel] increases.

2) The necessary subsidy 100 kmHSVS , [€/100 km] must be

bigger when utilizing more solar energy ( HSVfk is

decreasing). 3) There are feasible situations when the necessary subsidy

100 kmHSVS , [€/100 km] can annul. The fig. 3 diagram shows

three such situations: 8.0=HSVfk and ICE

fc = 1,1 [€ / l

fuel] ; 7.0=HSVfk and ICE

fc = 1,4 [€ / l fuel] and

6.0=HSVfk with ICE

fc = 2,2 [€ / l fuel].

Fig. 3. The necessary subsidy 100 km

HSVS , [€/100 km] versus the unitary fuel cost ICEfc [€ / l fuel].

8. FINAL CONCLUSION According to the done study there is a real feasible solution to make HSV profitable in the next future. This solution is characterized by the following numerical parameters: 1. The total cost of HSV HSV

PC =13000 €; 2. The total cost of classical car, powered by internal combustion engine, ICE

PC = 10000 €; 4. The operation-maintenance ratio of ICE car service (eq. 7), 05.0=ICE

OMs ;

5. The operation-maintenance ratio of ICE car product,

ICEOMp (eq. 11) and HSV

OMp -the operation-maintenance ratio

of HSV product, (eq. 12) 40.0== ICEOM

HSVOM pp ;

6. The investment cost of the HSV, ( )HSVp I

C = 4800 €;

3. The operation-maintenance ratio of HSV car service (eq. 8), 07.0=HSV

OMs ;

7. The consumption cost of the HSV, ( )HSVp C

C = 4800

€;

1 1.5 2 2.50

0.5

1

1.5

2

The

stat

e un

itary

subs

idy

of H

SV,

100 kmHSVS

, [€/

100k

m]

The unitary fuel cost, ICEfc [€ / l fuel]

8.0=HSVfk

7.0=HSVfk

6.0=HSVfk

HSVPC =13000 €;

ICEPC = 10000 €;


OMs ; 40.0== ICEOM

HSVOM pp ;

( )HSVp I

C = 4800 €; ( )HSVp C

C = 4800 €;

( )ICEp I

C = 4000 €; ( )ICEp C

C = 4000 €;

ICEf = 7 l / 100 km; ICEτ = HSVτ =75000 km


8. The investment cost of the ICE car,

( )ICEp I

C = 4000 €;

9. The consumption cost of the ICE car,

( )ICEp C

C = 4000 €;

10. The unitary fuel consumption of ICE, ICEf = 7 l / 100 km; 11. The total life cycle of an ICE car, ICEτ [km ICE / ICE car] and HSVτ -the total life cycle of a HSV, [km HSV / HSV car] ICEτ = HSVτ =75000 km. Of course, this is only one of the possible solutions. The done mathematical model presented here allows the modeling according to concrete possibilities the manufacturer has in order to achieve a better and better HSV. Modeling so, using the compared cost-to-quality analysis as work procedure, the authors are convinced that the best solution of a HSV is an ideal [12, 16, 17, 18], untouchable as any ideal, but an aim point for researchers.

REFERENCES

1. Arsie I., Di Domenico A., Marotta M., Pianese C., Rizzo G., Sorrentino M. (2005); A Parametric Study of the Design Variables for a Hybrid Electric Car with Solar Cells, Proc. of METIME Conference, June 2-3, 2005, University of Galati, RO.

2. Arsie I., Marotta M., Pianese C., Rizzo G., Sorrentino M. (2005); Optimal Design of a Hybrid Electric Car with Solar Cells, 1st AUTOCOM Workshop on Preventive and Active Safety Systems for Road Vehicles, Istanbul, Sept. 19-21, 2005.

3. Bejan A., e.a. (1996) – Thermal Design & Optimization, John Willey & Sons, New York

4. Frangopoulos, A. C, Caralis, C. Y., A method for taking into account environmental impacts in the economic evaluation of energy systems, Energy Conversion Management, Vol. 38, No. 15-17, 1997, pp. 1751-1763.

5. Juran, J. M., Godfrey, A. B., Juran's Quality Handbook (5th Edition), McGraw-Hill, 1999.

6. Ionita, C. I., Termoeconomia, stiinta interdisciplinara de minimizare a costurilor produselor prin intermediul exergiei (Thermo-economics, the Interdisciplinary Science which Minimizes the Product Cost by Means of Exergy). Conferinta de Termotehnica, 1996, Iasi. Termotehnica româneasca’96, vol. 1, pp. 35-39.

7. Ionita, C.I., Cernega, O., The Exergy-economic Analysis a Procedure to Minimize. Both the Products Costs and the Noxious. Emissions of the Power Plants. Heat Engines and

Environmental Protection, 25-28 May, 1997, Proceedings, Tata, Hungary, pp. 233-239.

8. Ionita, C.I. ,About the Application of Extended Exergy Analysis to the Optimization of Industrial Systems Using Cost/Quality Ratio, ECOS 2000 Proceedings, University of Twente, Nederland, pp. 187-198.

9. Ionita C.I., The Close Connection between Cost and Quality of Energy Products, ECOS 2001, Istanbul, Proceedings, vol. 2, pp. 813-820.

10. Ionita, C.I., The Cost-to-Quality Evaluation and Optimization of the Heat Powered Systems, HPC’01 2nd International Heat Powered Cycles Conference, CNAM Paris, France, Proceedings vol. II, pp. 255-262.

11. Ionita, C.I., Popa. V. The Analysis of the HVAC Systems Using Cost-to-Quality Criterion of Optimization, 7th REHVA World Congress Clima 2000, Napoli 2001, Proceedings on CD.

12. Ionita C.I. (2003), Engineering and Economic Optimization of Energy Production, International Journal of Energy Research, Article Reference No. 811, Journals Production Dept., 26: 697-715 (DOI: 10.1002/er.811), John Wiley & Sons, Ltd, Chichester, UK.

13. Ionita C.I., The Cost-to-Quality Ratio Based Optimization of the Energy Production, Entropie nr. 232, 2001, pp. 10-19.

14. Ioniţă, C.I., Extending thermo-economic analysis by cost to quality optimisation. Proceedings of ECOS 2002 July 3-5, 2002, Berlin, Germany, pp. 1434-1441.

15. Ionita C.I, Ion V.I. Cost-to-Quality Optimization of Refrigeration, NATO Advanced Study Institute, June 23-July 5, 2002, Altin Yunus-Cesme, Izmir-Turkiye, An International Meeting, Co-Directors: Prof S. Kakac and Prof. H. Smyrnov, ASI No.978410.

16. Ionita C.I., From Energy Analysis to Compared Cost-to-Quality Analysis of the Thermal Systems, Technical Sciences Academy of Romania, (2003), MOCM-9-vol.2, pp.149-155, ISSN 1224-7480.

17. Ionita C.I., Thermal Systems Optimization and Cost-to-Quality Analysis, International Journal of Heat and Technology”, vol. 22 nr. 1, 2004 pp. 27-37.

18. Ionita C.I., Beyond thermo-economic analysis of thermal systems: the compared cost-to-quality analysis, 1st International Conference on Thermal Engines and Environmental Engineering, METIME 2005, June 3-4, 2005, Galati, Romania.

19. http://www.toyota.co.jp/en 20. The Real Costs of Owning a Hybrid. www.edmunds.com/advice/fueleconomy/articles/103708/article.html- 44k 21. http://www.hybrid-car.org/


TECHNICAL AND ECONOMICAL FEASIBILITY STUDY OF A SMALL HYBRID VEHICLE FOR

URBAN TRANSPORTATION

C. Boccaletti(*), G. Fabbri(*), F. M. Frattale Mascioli(§), E. Santini(*)

(*)Department of Electrical Engineering, University of Rome “La Sapienza” (§)Department INFOCOM, University of Rome “La Sapienza”

Abstract: A technical and economical study has been carried out by the authors in order to assess the feasibility of the hybridisation of a small vehicle for urban transportation. An existing commercial vehicle powered by a 4kW internal combustion engine has been taken as a reference. A possible layout of the new hybrid propulsion system has been studied. Weights and volume occupancy have been examined. Initial and operating costs have been estimated and compared with the present market costs of the original vehicle. Performance calculations allowed to evaluate the vehicle behaviour in a standard mission and management aspects have been discussed. Copyright © 2002 IFAC Keywords: Hybrid Electric Vehicles, Urban transportation.

1. INTRODUCTION

In the last years the public perception of aspects related to the quality of life in urban centres has increased considerably, conditioning the individual choices and the administration policies. As a consequence, technical issues arising from the need to reduce the polluting emissions of vehicles are more and more important. According to the latest available national (Italian) data, road transportation is responsible for the higher percentage of NOx, CO and Non-Methanic-Volatile-Organic-Compounds (NMVOC) emissions, as shown in Table 1. If the contribution of these pollutants is splitted according to the type of vehicles, one can see that passenger cars are the main source of polluting emissions. For this reason, the problem of air quality trusted in the last years the demand for vehicles with a low impact to the environment (C. Boccaletti, L. Martellucci, 2001, K. Rajashekara et al., 2002, K. Rajashekara, 2004). Moreover, urban areas with restricted access are wider and wider, aiming to reduce the air pollution. Since these areas are usually those with the most intense traffic and the lowest number of parking places, the vehicle size is also important (F. Caricchi et al., 2003). In the following,

a technical and economical study to assess the feasibility of the hybridisation of a small vehicle for urban transportation is described.

2. THE ORIGINAL VEHICLE The vehicle chosen for the hybridisation is a small commercial vehicle suitable for city service (see Fig. 1). This kind of vehicle is particularly conceived to be used in the narrow streets of historical centres and to make parking easier. The technical data and size of the vehicle are listed in Tabs 2 and 4, respectively. Two points of the characteristic curve are reported in Tab. 3. The engine and the other components of the existing (traditional) propulsion system are located in the front. Owing to the reduced size of the vehicle, the various element are disposed in such a way that the volume occupancy is optimised and the insertion of a new bulk elements would be difficult. The bonnet or the load deck (in the pickup version) are located in the rear. A mean market price of the vehicle range of 8000 € can be taken as a reference.


Table 1 Contribution of road transportation to polluting emissions in 2002 (%)

SOX NOX NMVOC CO CO21.95 48.81 32.31 65.27 27.85

Source: Elaboration from Apat (2006)

Fig. 1. The commercial vehicle chosen for the

hybridisation.

Table 2 Features of the commercial vehicle chosen for the hybridisation

Engine Diesel N° cylinders 2 Cyl. Volume 505 cm3

Cycle 4 Strokes Cooling Type liquid Max. Power 4 kW @ 3600 rpm Max. Torque 14 Nm @ 2400 rpm Transmission continuous variator with pulleys

and centrifugal masses Gear Position Inward / Backward / Idle Traction Front wheels with inverter

differential Electric Circuit Voltage

12 V

Max. Speed 45 Km/h Max. Slope > 25% Table 3 Performance data of the commercial vehicle

chosen for the hybridisation

rpm Torque [Nm] Power [W] 2400 14 3500 3600 10.61 4000

Table 4 Size and weight of the commercial vehicle

chosen for the hybridisation Length 3224 mm Width 1378 mm Heigth 1487 mm Mass 349 kg Admissible Mass 675 kg

3. THE HYBRIDISATION The expected benefits of the hybridisation are: − Reduction of fuel consumption; − Reduction of polluting emissions; − Increased performance. 3.1 Parallel configuration

The first configuration of the hybrid system taken as a reference is of the parallel type. In general, this configuration is considered suitable for small vehicles. The scheme of the propulsion system includes a power-split drive train. According to the complexity of such a device, together with other considerations, the choice of a parallel configuration could be not suitable to the series production in a small enterprise with affordable costs and therefore an acceptable commercial price. The configuration has been studied for a specific use of the vehicle in an urban environment, with limited flexibility. In case of missions that are quite far from the city standards, the availability could not be assured. In the particular case of these vehicles, the Italian law prescribes a maximum speed of 45 km/h, therefore even the European standard for motorcycles (ECE47) could not be taken as a reference, because it provides a maximum speed of 50 km/h. However, non-conventional cycles have been proposed for the analysis of the vehicle behaviour in an urban environment, and among these one with a maximum speed of 45 km/h (Avella, 2000) (see Fig. 2). This cycle has been considered for our analyses.

Fig. 2. Urban cycle in heavy traffic conditions. Chosing a Hybridisation Factor (HF) of 25%, the 1 pole, 60 Hz syncronous motor has a power of 1 kW. The electromagnetic torque is 3.18 Nm. The storage system should have a capacity of at least 1.1 kWh. Lead-acid batteries (not too expensive, with a quite long life), including supports and connections, should weight about 30 kg, with a volume of 10 litres. The voltage is 48 V. Lithium batteries could be an alternative, with less weight and volume occupancy. An inverter suitable for the application has the features of Table 5. Considering an efficiency of the charge/discharge cycle of 80% and a battery charge efficiency of 90%, the electric energy consumption is about 1.5 kWh per cycle (i. e., per day), corresponding to 0.26 € or 0.0052 €/km, if 50 km is the mean daily run. The battery cost is some 0.05 €/Wh. Therefore, 55 € are enough to ensure the provided run in the first period of operation. However, the capacity decreases of 0.04% per cycle, so that after 365 cycles (one year), the daily run is reduced. Usually, in this case the driver increases the frequency of the charge cycles instead of changing the batteries. In so doing, the battery aging becomes faster and faster. In the most favourable case, with an optimum management of charge/discharge cycles, one can think to reach a battery life of 5 years, which corresponds to 2000 kWh stored and 475 €. Including the initial cost of the batteries, the cost per


year is 106 € and 0.0058 €/km. The above costs are calculated with the electric propulsion as the only one. Considering a 20% saving in hybrid mode, thanks to the energy recovered during braking and deceleration, the cost can be reduced to some 0.0046 €/km. For city service, the cost of fuel is about 0.056 l/km, or 0.07 €/km. In hybrid mode, the fuel consumption can be reduced of about 20%, obtaining a cost of 0.05 €/km. Therefore, the total operation cost of the hybrid vehicle can be calculated in about 0.0546 €/Km. To redeem 2000 € (difference with the price of a traditional vehicle), the vehicle should run for 130000 km, corresponding to about 7 years at 50 km/day. During this period, one substitution of the batteries has to be considered (55 €). In conclusion, the complete amortization is attained at the end of the vehicle life. Therefore, the user should not benefit by significant economic advantages. On the other hand, from the point of view of the environmental impact a significant reduction of polluting emissions can be obtained. The above rough considerations, however, do not take the possible additional production costs into account, due to the choice of the power-split drive train, needed for the coupling among the propulsion and traction devices (L. Martellucci et al., 2001). Moreover, the realisation of the drive and of the relevant control system could require particular technical skills, that are not always available in a small enterprise. Although quite simplified, the above results show that in this particular case the parallel configuration does not have wide margins of application, from both an industrial and customer’s point of view.

Table 5 Inverter features Voltage 12 Vdc or 24 Vdc ±15%

Power range 300 VA÷12 kVA with intermittent service

Efficiency 71-77% Output voltage 220 Vac In order to increase the availability of the hybrid vehicle also for missions quite far from the urban cycle taken as a reference in the design phase, a series configuration can be chosen. The latter includes more components to be located into the vehicle than the parallel solution, but the layout is subject to less constraints. Moreover, the components do not differ from commercial devices, whose assemblage requires standard technical skills. 3.2 Series configuration

As said above, in this configuration there is no mechanical coupling between the Internal Combustion Engine (ICE) and the wheels, reducing the constraints of the layout, and this is particularly important for a small vehicle. However, there are more components than in the parallel case, and more space is needed for the batteries. The volume of the engine bonnet in the original vehicle is not so large, so that the electric motor, the inverter and the batteries cannot be mounted in the same place. In

Fig. 3 a sketch of the proposed layout is shown. The generator is positioned in the front engine bonnet, the electric motor is connected to the rear wheel axis, batteries and converters are in the rear coffin.

Fig. 3. Layout of the hybrid series propulsion system. In order to choose the component size, the required performance have to be considered. As above stated, the vehicle has a maximum speed of 45 km/h. The aerodynamic, mechanical and rolling resistances - the latter including both the rolling friction and the tyre deformation - contribute to the total resistance to the vehicle motion. Such a resistance can be calculated through expressions containing empirical coefficients. For our case, the rolling resistance is assumed proportional to vehicle weight W. The reference weight for the performance calculation is assumed to be 500 kg. It follows

Ftyre = 8·g·W·10-3 = 8·9.81⋅0.5 = 39.24 N (1) The aerodynamic resistance can be calculated as Faer=0.5·Cr·ρ·A·V2=0.5·0.3·1.2·2.0·12.52=56.25 N (2)

being Cr a drag coefficient, ρ the air density (kg/m3), A the reference front section area (m2) and V the maximum vehicle speed (m/s). Total resistance R is 95.49 N. The corresponding torque at the wheels is

T = R·d = 95.49·0.252 = 24.06 Nm (3) being d the wheel radius (m). Therefore, the required power is

P = T·ω = 24.06·7.88·2π = 1.19 kW (4) Since the chosen electric motor has a rated power of 4.2 kW, one can calculate the maximum slope the vehicle can climb at the maximum speed. The available additional power is 3.01 kW. It follows

Max Slope% = 3.01·3600/(500·9.81·45) = 4.9 (5) One can also calculate at what speed the vehicle can move up a slope of 10%, the standard value for continuous running. In this case the rated power of the electric motor allows to attain a speed of about 26 km/h. Up a slope of 20% the maximum speed is about 15 km/h. The electric motor has a rated torque of 40 Nm @ 1000 rpm and a maximum torque of


80 Nm. The weight is about 15 kg and the height and axial length are about 25 cm and less than 30 cm, respectively. The battery pack consists of 5 lead gel batteries of 30 Ah, 12 V nominal. The total weight is 53 kg. Height, length and width of a single battery are 15.5 cm, 19.5 cm and 13.3 cm, respectively. The no-load voltage and the charge and discharge resistances as a function of the State-Of-Charge (SOC) are given in Table 6.

Table 6 Battery characteristics

SOC V0 Rdis Rchg

0.1 11.28 0.0268 0.0373 0.2 11.58 0.0163 0.0259 0.3 11.88 0.0124 0.0201 0.4 12.06 0.0107 0.0173 0.5 12.18 0.01 0.0166 0.6 12.36 0.01 0.017 0.7 12.54 0.01 0.0196 0.8 12.72 0.0114 0.0243 0.9 13.02 0.0114 0.0348 1 13.5 0.011 0.1141

The diesel generator has the characteristics of Table 7. The efficiency can be evaluated through the curves of power and specific fuel consumption given by the manufacturer with reference to the ISO 3046/1-IFN standard (see Fig. 4). The maximum efficiency corresponds to a power of about 4.1 kW.

Table 7 Engine characteristics N° cylinders 1 Cyl. Volume 315 cm3

Max. Power 5 kW @ 3600 rpm Max. Torque 15 Nm @ 2400 rpm

1.5 2 2.5 3 3.5 4 4.5

0.27

0.28

0.29

0.3

0.31

0.32

Power [kW]

Effi

cien

cy

Fig. 4. Engine efficiency vs. engine power. In order to minimise the fuel consumption, a control strategy has to be chosen. Once fixed a SOC admissible range, the best operating point of the generator as a function of the power required by the drive is calculated minimising the fuel consumption (S. Barsali et al., 2002).

When operating in ON-OFF mode, the DC source logic is based on the battery SOC. The optimisation procedure consists of calculating average drive power demand Pd in a given time interval t, estimating the battery SOC in t, defining the operating state (ON or OFF) and finally calculating - if the state is ON - reference power Ps* as the power to be generated by the DC source corresponding to the maximum generation efficiency. Based on the values of Table 6, a global battery efficiency ηb = ηchgηdis has been estimated. A value of 0.85 has been assumed. The generation efficiency is defined as (S. Barsali et al., 2002)

( )gen

s

bbbdgl P

PPPη

ηη

)1( −−+= (6)

The goal is to obtain the value of the average power to be delivered by the DC source as a function of average drive power demand Pd. For each Pd, the value of Ps corresponding to the maximum of ηgl can be individuated. Thus, function Ps* = Ps*(Pd) can be obtained. An efficiency of 0.9 has been assumed for the DC (electric generator-inverter) generation system. The values of Fig. 4 have been multiplied by this efficiency, the procedure has been applied, and the curve of Fig. 5 has been obtained. It shows the average power to be delivered by the DC source vs. Pd, in order to have the minimum fuel consumption and to keep the battery SOC within the admissible - “safety” - range. Beyond the minimum point, on the right of the graph, the continuous operation (“load following”) substitutes the ON-OFF mode and no energy is stored in the battery pack.

1.5 2 2.5 3 3.5 4 4.54

4.05

4.1

4.15

4.2

4.25

4.3

4.35

4.4

4.45

4.5

Drive power [kW]

Sou

rce

pow

er [k

W]

Fig. 5. Optimal DC source operation curve. The effects of the above control strategy on the global efficiency (M. Pasquali, G. Pede, 2006) are shown in Fig. 6.


1.5 2 2.5 3 3.5 4 4.50.24

0.245

0.25

0.255

0.26

0.265

0.27

0.275

0.28

0.285

0.29

Drive power [kW]

Glo

bal e

ffici

ency

optimisedload following

Fig. 6. Global generation efficiency curves in case of

optimised control (blue) and load following (green).

As above said, in the ON-OFF mode the generator operates in optimum conditions and a part of the produced energy is stored in the batteries. From Fig. 5, one can see that the delivered power varies only between about 4.05 and 4 kW in the ON-OFF mode and within about 4 and 4.5 kW in the load following mode. However, for urban use the power demand of this kind of vehicle can hardly overcome 4 kW, unless additional power is required by auxiliary devices (C. Boccaletti, L. Martellucci, 2001). Thus, the generator practically operates at a fixed point, corresponding to the best efficiency. For a given vehicle mission, like that of Fig. 2, power Pb stored in the batteries can be calculated at every time instant, as the difference between generated power Ps and drive power demand Pd (see Fig. 6). An efficiency of 0.85 can be assumed for the electric drive. According to the battery SOC, the generator should be switched on or off to keep the SOC within the fixed range, say 0.4÷0.85. In this way it is possible to calculate the energy produced by the generator during a complete charging/discharging cycle of the batteries, and the relevant noise and fuel consumption. An evaluation of polluting emissions could be performed by means of maps given by the manufacturers, but an actual comparison with the dynamical behaviour of the original propulsion system is possible only on the basis of an experimental on-road campaign (Avella, 2000). However, a significant reduction of polluting emission is expected, thanks to the limited operating time of the engine, nearly in conditions of best efficiency, covered distances being equal. A software program has been set up to calculate the number of standard urban missions (and then the total covered distance) corresponding to a complete charging/discharging cycle of the batteries within the admissible range, and the relevant fuel consumption. A maximum noise level of 78 db has been calculated from manufacturer’s data, corresponding to the engine operating conditions.

Fig. 6. Main components and relevant power fluxes. Starting from the established minimum SOC level (0.4), the batteries are charged until the admissible limit. At that point the generator is switched off, and the drive power demand makes the stored energy decrease, until the minimum SOC is attained again. The charging/discharging cycle of the batteries is completed in about 25 standard urban missions, corresponding to a distance of 24.5 km. The fuel consumption is about 160 g, and the total produced energy is about 0.4 kWh. From the above considerations, it comes out that such configuration corresponds to a large flexibility and availability of the hybrid vehicle, allowing its use also for missions quite far from the urban cycle taken as a reference in the calculations. Finally, the cost of the main components of the new propulsion system can be estimated between 1750 and 2250 €, according to the cost of the generator, being the cost of the battery pack some 250 € and that of the electric drive some 500 €.

4. CONCLUSIONS An existing commercial vehicle powered by a 4kW internal combustion engine has been taken as a reference for a preliminary technical – economical analysis of possible hybrid configurations. Weights, volume occupancy and costs of a parallel and a series layout have been estimated. A particular urban mission, suitable for this kind of vehicles in both configurations, has been individuated. Some aspects of the vehicle management have been discussed with particular reference to the series configuration, and performance calculations allowed to evaluate the characteristics of the propulsion system related to its availability also for missions quite far from the standard one. A significant reduction of polluting emission is expected in both cases with respect to the original (traditional) propulsion system. From both an industrial and customer’s point of view, in the particular case examined the series configuration seems to have wider margins of application, although a final answer could come only from more in-depth economical analyses.

5. REFERENCES F. Avella (2000)– “L’attivita’ sperimentale della

stazione sperimentale per i combustibili per la


valutazione delle emissioni generate dagli autoveicoli” – Proc. of Seminario ANPA, Rome, Italy (in Italian)

L. Martellucci, M. Santoro, C. Boccaletti (2001) - “A Powertrain with Planetary Gear System: Advantages and a Design Approach” – Proc. of EVS 18 – The 18th International Electric Vehicle Symposium, Berlin, Germany

C. Boccaletti, L. Martellucci (2001) – “Study of an air conditioning system for a small hybrid vehicle based on the absorption principle” – SAE Paper 2001-01-3808, Proc. of Congresso SAE Brasil 2001, São Paulo, Brazil

K. Rajashekara et al. (2002) - “Comparative study of new on-board power generation technologies for automotive applications,” in Proc. IEEE Workshop Power Electronics in Transportation, Auburn Hills, MI, pp. 3–10

S. Barsali, M. Pasquali, G. Pede (2002) - "Definition of Energy Management Technique for Series Hybrid Vehicles" - Proc. of EVS 19 – The 19th International Electric Vehicle Symposium, Pusan, Korea

F. Caricchi, L. Del Ferraro, F. Giulii Capponi, O. Honorati, E. Santini (2003) – “Three-Wheeled Electric Maxi-Scooter for Improved Driving Performances in Large Urban Areas” - Proc. of 2003 IEEE International Electric Machines and Drives Conference, IEMDC’03, Madison, Wisconsin, USA

K. Rajashekara (2004) – “Hybrid and Fuel Cell Systems for Transportation”, Meeting IV of IEEE IASChapter, Hungary

M. Pasquali, G. Pede (2006) – “Ottimizzazione della gestione energetica di un veicolo ibrido di tipo serie” – Proc. of 17th Seminario Interattivo ANAE, “Azionamenti Elettrici Evoluzione Tecnologica e Problematiche Emergenti”, Bressanone, Italy (in Italian)


PASSIVITY-BASED CONTROL OF HYBRIDSOURCES APPLIED TO A TRACTION

SYSTEM

Damien Paire ∗, Mohamed Becherif ∗∗,Abdellatif Miraoui ∗

∗ L2ES, UTBM, Belfort (cedex) 90010, FRANCE∗∗ SeT, UTBM, Belfort (cedex) 90010, FRANCE

[email protected]:+33 (0)3 84 58 33 96, Fax:+33 (0)3 84 58 34 13

Abstract: Nowadays, energy management becomes an economic and technicalissue. To reduce systems consumption, the idea is to recover energy when itis possible and to reuse it depending on the demand. To save energy, storagecomponents (supercapacitors here) are needed to absorb or supply power picks.This article present an hybrid system suppling an electromotive force. In orderto supervise the power flows in the system, Passivity-Based Control is used anddifferent configurations are simulated.

Keywords: energy recovery, hybrid system, Passivity-Based Control, embeddedenergy, supercapacitors

1. INTRODUCTION

In electric traction systems (like vehicles, eleva-tors, . . . ), if the load is supplied using a single en-ergy source, it has to answer to all solicitations ofthe load. Thus, the source has to supply or absorbthe picks of power resulting from accelerations andbraking. So, the source has to provide energy andpower, this is strongly penalizing. In order to opti-mize the power transfer and to improve equipmentlifetime, supercapacitors (SC) and different kindof DC sources can be hybridized. Then the SCsupply or absorb power picks and the DC sourceprovide the average power.

In this paper, a hybrid power source using DCsource (obtained from network or from batteriesalone or associated with photovoltaic panels) andSC supplying a load is proposed. In a first step,a dynamic modeling of the system is given. Ina second step, this system is written in a PortControlled Hamiltonian (PCH) form where im-

portant structural properties are exhibited. Thena Passivity-Based Control (PBC) of the system ispresented proving the global stability of the equi-librium with the proposed control laws. Finally,simulation results using Matlab are given.

2. HYBRID DC SOURCE SYSTEM

2.1 Structure of the hybrid source

As shown in Figure 1, the studied system com-prises a DC link supplied by a DC source and ano reversible DC-DC Boost converter which main-tains the DC voltage VDC to its reference valueV DC and a SC storage device which is connectedto the DC link through a current reversible DC-DC converter. The load consist of a resitor RL,a inductor LL and an electromotive force (emf)E. This structure is used to model merely anelectrical machine.


LL

E

RL

LN LDC iLiDCiN

LSC

VN

VSC

VDC

iSC

VS

CSC

TN

TSC

T SC

CS CDC

Fig. 1. System electrical model

The function of the DC source is to supply themean power to the load, whereas the storagedevice is used as a power source: it supplies andabsorbs peak loads required during accelerationand braking. In order to manage energy exchangesbetween the DC link and the storage device, threeoperating modes are defined:

• Charge mode, in which the main source sup-plies energy to the storage device,

• Discharge mode, in which the storage deviceand the main source supply energy to theload,

• Recovery mode, in which the load suppliesenergy to the storage device.

2.2 State space model of the system

The model of the hybrid system can be writtenin a state space model by choosing the followingvariables:

x =[x1, x2, x3, x4, x5, x6, x7

]T

=[VS , iN , VDC , iDC , VSC , iSC , iL

]T

The control vector is:

u =[u1, u2

]T =[uN , uSC

]T (1)

where uN and uSC ∈ [0, 1].u = 1 means the associated transitor is closed andu = 0 means the associated transitor is opened.

The 7th order overall state space model is then :

x1 =1

CS[(1 − u1)x2 − x4]

x2 =1

LN[VN − (1 − u1)x1]

x3 =1

CDC[x4 − x7 + (1 − u2)x6]

x4 =1

LDC[x1 − x3] (2)

x5 =−1CSC

x6

x6 =1

LSC[x5 − (1 − u2)x3]

x7 =1

LL[x3 − RLx7 − E]

y = x3

2.3 Equilibrium

After some simples calculations the equilibriumvector is:

x =[x1, x2, x3, x4, x5, x6, x7

]T (3)

=[

Vd,(Vd − E)Vd

RLVN, Vd,

Vd − E

RL, x5, 0,

Vd − E

RL

]T

Where Vd is the desired DC Bus voltage. An im-plicit purpose of the proposed structure (Figure 1)is to recover energy to charge the SC. Hence, thedesired voltage x5 = VSC(t = 0) = 12V .

u =[uN , uSC

]T =[

1 − VN

Vd, 1 − x5

Vd

]T

(4)

The natural energy function of the system is:

H =12xT Qx (5)

where Q = diagCs ; LN ; CDC ; LDC ; CSC ; LSC ; LL is adiagonal matrix.

3. PROBLEM FORMULATION

After system modeling, equilibrium points arecomputed in order to ensure the desired behaviourof the system. When steady state is reached, theload has to be supplied only by the DC source. Sothe controller has to maintain the DC bus voltageto a constant value and the SC current has to becancelled.During transient, the power delivered by the DCsource has to be the more constant as possible(without a significant power peak), so the SCdeliver the transient power to the load. If theload provide current, the SC recover its energy.At equilibrium, the SC has to be charged and the


current has to be equal to zero.In the next section, a controller will be found andthe system’s stability will be prouved.

4. PORT-CONTROLLED HAMILTONIANREPRESENTATION OF THE SYSTEM

PCH systems were introduced by [1] and hassince grown to become a large field of interest inthe research of electrical, mechanical and electro-mechanical systems. A recent and very interestingapproach to solve these problems is the IDA-PBCmethod, which is a general way of stabilizing alarge class of physical systems, see [2, 4].

The desired closed loop energy function is:

Hd =12xT Qx (6)

where x = x − x is the new state space definingthe error between the state x and its equilibriumvalue x. So according to the state space model (2),the following equations can be written:

˙x1 =1

CS[(1 − u1)(x2 + x2) − x4 − x4]

˙x2 =1

LN[VN − (1 − u1)(x1 + x1)]

˙x3 =1

CDC[(x4 + x4) − (x7 + x7)

+(1 − u2)(x6 + x6)]

˙x4 =1

LDC[(x1 + x1) − (x3 + x3)] (7)

˙x5 =−1CSC

(x6 + x6)

˙x6 =1

LSC[(x5 + x5) − (1 − u2)(x3 + x3)]

˙x7 =1

LL[(x3 + x3) − RL(x7 + x7) − E]

The PCH form of studied system with the newvariable x and in function of the gradient of thedesired energy (6) is:

˙x = (J (u1, u2) −R) .∇Hd + Ai(x, u) (8)

where

J (u1, u2) −R =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

01 − u1

CsLN

0−1

CsLDC

0 0 0

− 1 − u1

CsLN

0 0 0 0 0 0

0 0 01

CDC LDC

01 − u2

CDC LSC

−1

CDCLL

1

CsLDC

0−1

CDC LDC

0 0 0 0

0 0 0 0 0−1

CSCLSC

0

0 0 − 1 − u2

CDC LSC

01

CSCLSC

0 0

0 01

CDC LL

0 0 0−RL

L2L

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

∇Hd =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎣

Csx1

LN x2

CDC x3

LDC x4

CSC x5

LSC x6

LLx7

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎦

Ai(x, u) =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

(1 − u1)x2 − x4

Cs

VN − (1 − u1)x1

LN

x4 − x7 + (1 − u2)x6

CDC

x1 − x3

LDC

−x6

CSC

x5 − (1 − u2)x3

LSC

x3 − RLx7 − E

LL

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

J (u1, u2) = −J T (u1, u2) is a skew symmetricmatrix defining the interconnection between thestate space and R = RT ≥ 0 is symmetric positivesemi definite matrix defining the damping of thesystem.

Ai(x, u) evaluated at the equilibrium points (3)gives:


Ai =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

(E − Vd)(VN − (1 − u1)Vd)RLVNCs

VN − (1 − u1)Vd

LN

0

0

0

x5 − (1 − u2)Vd

LSC

0

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

The following control laws are proposed:u1 = u1

u2 = u2 − rx6(10)

where r is a design parameter (r ≥ 0).

Proposition 1. The origine of the closed loop PCHsystem (8), with the control laws (10) and (4)with the radially unbounded energy function (6),is globally asymptotically stable.

Proof. The closed loop dynamic of the PCH sys-tem (8) with the laws (10) and (4) with the radi-ally unbounded energy function (6) is:

˙x = [J (u1, u2) −R′]∇Hd (11)

where J (u1, u2) −R′ =⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

01 − u1

CsLN

0−1

CsLDC

0 0 0

− 1 − u1

CsLN

0 0 0 0 0 0

0 0 01

CDC LDC

01 − u2

CDCLSC

−1

CDC LL

1

CsLDC

0−1

CDC LDC

0 0 0 0

0 0 0 0 0−1

CSCLSC

0

0 0 − 1 − u2

CDC LSC

01

CSCLSC

− rVd

L2SC

0

0 01

CDC LL

0 0 0−RL

L2L

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

R′ = R′T ≥ 0. The derivative of the desiredenergy function (6) along the trajectory of (11)is:

Hd = ∇HTd

˙x = −∇HTd R′∇Hd ≤ 0 (12)

5. SIMULATIONS

5.1 Load works as a receiver

The following simulations present the system re-sponse and control obtained with the proposed

control laws (10). In this case, the load is con-sidered as a receiver. To illustrate the controllerefficiency, the DC bus voltage reference, the elec-tromotive force (emf) and the resistance are mod-ified (see Figure 5 and Figure 6). The DC busvoltage is initialized at 36V and the DC Busvoltage reference is set at 42V at the beginning.

Figure 2 presents the system response to changesin the DC Bus voltage reference (Vd), emf (E)and load current iL. The DC Bus voltage trackswell the reference, i.e. very low overshoot and nosteady state error are observed.

0 1 2 3 4 5 6

35

40

45

50

t(s)

Vd &

VD

C(V

)0 1 2 3 4 5 6

0

0.5

1

1.5

2

2.5

t(s)

i L(A)

Fig. 2. (a) DC Bus voltage and its reference. (b)Load current.

Figure 3 shows the source voltage (VN ) and cur-rent (iN ). In our modeling, we assume that theDC source is ideal, thus VN stay at constant valueregardless of the current iN . A smooth behaviorof the current is observed regarding the changes inVd, E and RL, because the SC supply the transientpower.

0 1 2 3 4 5 614

14.5

15

15.5

16

t(s)

VN

(V)

0 1 2 3 4 5 60

2

4

6

8

t(s)

i N(A

)

Fig. 3. (a) DC source voltage. (b) DC sourcecurrent.

Figure 4 shows the SC voltage and current re-sponses. The SC supply power to the load in thetransient and in the steady state no power or


energy is extracted since the current iSC is nul.The positive sens of iSC means that the SC supplythe load and the negative one corresponds to therecover of energy by the SC. At time t = 4s, theSC absorb the current pick to respond quickly tothe fast DC reference change.

0 1 2 3 4 5 611.998

11.999

12

t(s)

VS

C(V

)

0 1 2 3 4 5 6−6

−4

−2

0

2

4

6

t(s)

i SC

(A)

Fig. 4. (a) SC voltage. (b) SC current.

Figure 5 and Figure 6 present the network Boostcontroller, the SC bidirectional converter con-troller, the changes in the load resistance RL andin emf. UN and USC are in the set [0, 1].

0 1 2 3 4 5 60.55

0.6

0.65

t(s)

UN

0 1 2 3 4 5 60.6

0.65

0.7

0.75

0.8

t(s)

US

C

Fig. 5. (a) Source Boost control. (b) SC convertercontrol.

Figure 7 presents the power transfers in the sys-tem. Power pick are absorbed or supplied bySC, thus a smooth power is provided by the DCsource. This can reduce significantly the harmon-ics on the line.

It can be seen from Figure 2 that the system withthe proposed controller is robust towards loadresistance changes and emf variations.

5.2 Load works as a generator

The following simulations present the system re-sponse when the load is considered as a generator.

0 1 2 3 4 5 67

8

9

10

11

t(s)

RL(Ω

)

0 1 2 3 4 5 6

20

25

30

t(s)

E(V

)

Fig. 6. (a) Load resistance change. (b) Load emfchange.

0 1 2 3 4 5 6−60

−40

−20

0

20

40

60

80

100

t(s)

Pow

er (

W)

SC

SC charge

SC discharge

Load

DC source

Fig. 7. Power transfers

So, the proposed control laws can be tested duringrecovery mode (between t=1s and t=4), only theelectromotive force (emf) is modified for thesesimulations. The DC bus voltage is initialized at36V and the DC Bus voltage reference is set at42V.

Figure 8 presents the system response to changesin the emf (E). The DC Bus voltage tracks wellthe reference during the first second, then a smallovershoot and a steady state error are observedwhen the load current becomes negative. This is a7% error which is acceptable in most of the case,an improvement will be presented in section 6 tocancel this error.

Figure 9 shows the source voltage and current. VN

stay at constant value, as it is explained in thelast simulations (5.1). A smooth behavior of thecurrent is observed regarding the changes in E,this is because the SC supply the transient power.When the load provides energy, all goes to theSC because the DC-DC source converter is notreversible.


0 1 2 3 4 5 6

35

40

45

50

t(s)

Vd &

VD

C(V

)

0 1 2 3 4 5 6−1

0

1

2

3

t(s)

i L(A)


0 1 2 3 4 5 614

14.5

15

15.5

16

t(s)

VN

(V)

0 1 2 3 4 5 60

2

4

6

8

t(s)

i N(A

)


All the current provided by the load is absorbedby the SC during the recovery mode, as shownFigure 10. The SC supply power to the load inthe transient like it was shown in section 5.1. TheSC voltage increase when the load works as agenerator.

0 1 2 3 4 5 611.995

12

12.005

12.01

12.015

t(s)

VS

C(V

)

0 1 2 3 4 5 6

−5

0

5

t(s)

i SC

(A)


Figure 11 shows the emf changes and the controlsignals of the converters.

0 1 2 3 4 5 60.6

0.7

0.8

t(s)

UN

0 1 2 3 4 5 60.6

0.7

0.8

t(s)

US

C

0 1 2 3 4 5 6

20

30

40

50

t(s)

E(V

)

Fig. 11. (a) Source Boost control. (b) SC convertercontrol. (c) Load emf change.

Figure 12 presents the power transfers in thesystem. As in Figure 7, power pick are absorbed orsupplied by SC, so a smooth power is provided bythe DC source. During the energy recovery, all thepower coming from the load goes to the SC andthe DC source provides a very low power (due tothe source converter model).

0 1 2 3 4 5 6−60

−40

−20

0

20

40

60

80

100

t(s)

Pow

er (

W) DC source

SC

Load


The system behaviour follows requirements devel-oped in section 3.

6. IMPROVEMENT

6.1 New control

In the last solution, only one measure (iSC) wasdone. In order to cancel the steady state error onthe DC bus voltage, a integrator can be added. DCbus voltage (VDC) has to be known so its measureis necessary. The integrator action is added in thecontrol equation u2 (10) and allows to reduce the


error between VDC and Vd. So the new controllaws are: ⎧⎨

⎩u1 = u1

u2 = u2 − rx6 − Ki

∫x3

(13)

The stability proof is given in [8]. Since theclose loop system is stable, the addition of anintergrator do not modify the stability. In the nextpart, the results are presented.

6.2 Simulations

For the simulations, the same configuration as in5.2 is chosen, new control (13) is applied.

Figure 13 presents the system response to changesin the emf (E). The steady state error is can-celled with this new control but there is still anovershoot around 8V. The current value is verysimilar to the one Figure 8, except during therecovery mode. Its value is different because DCbus voltage is maintained at 42V.

0 1 2 3 4 5 6

35

40

45

50

t(s)

Vd &

VD

C(V

)

0 1 2 3 4 5 6−1

0

1

2

3

t(s)

i L(A)


As shown Figure 14, during the energy recovery,the DC source current goes close to zero becausethe DC-DC converter is not reversible. A smallovershoot of the current is observed when the DCsource start to provide energy to the system (att=0s and t=4s).

Figure 15, the SC still provide transients, but donot go to zero during steady state. This is due tothe new term in the control equation 13. So whenthe load absorbs energy, the DC source and theSC provide it together.

The same thing can be underline on Figure 16,the load power is the sum of SC and DC sourcepower during steady state.

Figure 17 shows the emf changes and the controlsignals of the converters.

0 1 2 3 4 5 614

14.5

15

15.5

16

t(s)

VN

(V)

0 1 2 3 4 5 60

2

4

6

8

t(s)

i N(A

)


0 1 2 3 4 5 611.99

12

12.01

12.02

t(s)V

SC

(V)

0 1 2 3 4 5 6

−5

0

5

t(s)

i SC

(A)


0 1 2 3 4 5 6−60

−40

−20

0

20

40

60

80

100

t(s)

Pow

er (

W) DC source

SC

Load


7. CONCLUSION

A modeling of hybrid sources system composedof a DC energy source and SC power source ispresented. PCH structure of the overall system isgiven exhibiting important physical properties interms of variable interconnection and damping ofthe system. The problem of the DC Bus Voltage


0 1 2 3 4 5 60.6

0.7

0.8

t(s)

UN

0 1 2 3 4 5 60.6

0.7

0.8

t(s)

US

C

0 1 2 3 4 5 6

20

30

40

50

t(s)

E(V

)

Fig. 17. (a) Source Boost control. (b) SC convertercontrol. (c) Load emf change.

control is solved using simple linear controllerbased on an IDA-PBC approach.An important property has to be underline, onlyiSC measure is needed for the first controller (10).Global stability proof is given and encouragingsimulation results has been obtained. Many bene-fits can be expected from the proposed structuresuch that supplying and absorbing the power picksby using SC which also allow recovering energy. Atthe same time, this can reduce significantly theharmonics on the line.Finally, two sensors (instead of one) are used tocancelled the steady state error with an integrator(13). Thus depending of the application require-ments, a solution with one sensor can be chosenor a second solution with two sensors.

REFERENCES

[1] A.J van der Schaft, B.M. Maschke, “On thehamiltonian formulation of nonholonomic me-chanical systems”, Reports on MathematicalPhysics, vol.34, no.2, pp.225-233, 1994.

[2] R. Ortega, A. Loria, P.J. Nicklasson, andH. Sira-Ramirez, “Passivity-based controlof Euler-Lagrange systems,” in Commu-nications and Control Engineering. Berlin,Germany:Spring-Verlag, 1998.

[3] R. Ortega, A.J van der Schaft, B. Maschkeand G. Escobar, “Interconnection and damp-ing assignment passivity-based control of port-controlled hamiltonian systems,” Automaticavol.38, pp.585-596, 2002.

[4] M. Becherif and E. Mendes, “Stability androbustness Disturbed-Port Controlled Hamil-tonian system with Dissipation,” 16th IFACWorld Congress, Prague ,2005,

[5] S.M. Halpin and S.R. Ashcraft, “Design con-siderations for single-phase uninterruptiblepower supply using double-layer capacitors asthe energy storage element” IEEE-IAS, SanDiego, 1996, v4, pp 2396–2403

[6] M. Becherif, M.Y. Ayad and A. Miraoui,“Modeling and Passivity-Based Control of Hy-brid Sources: Fuel Cell and Supercapacitors”41st IEEE-IAS, 2006

[7] M. Becherif, “Passivity-Based Control of Hy-brid Sources: Fuel Cell and battery” 11th IFACSymposium on Control in Transportation sys-tems, 2006

[8] R. Ortega and E. Garcia-Canseco, “Intercon-nection and Damping Assignment Passivity-Based Control: A Survey”, European Journalof Control, 2004


HYBRID ELECTRIC VEHICLES :FROM OPTIMIZATION TOWARD REAL-TIME

CONTROL STRATEGIES

Gregory Rousseau ∗,∗∗ Delphine Sinoquet ∗

Pierre Rouchon ∗∗

∗ Institut francais du petrole, 1 et 4, avenue de Bois-Preau,92852 Rueil-Malmaison Cedex - France

∗∗ Ecole des Mines de Paris

Abstract: Hybrid-electric vehicles appear to be one of the most promising tech-nologies for reducing fuel consumption and pollutant emissions. The presentedwork focuses on two types of architecture : a mild hybrid and a full hybrid wherethe kinetic energy in the breaking phases is stored in a battery to be re-usedlater via the electric motor. This additional traction power allows to downsizethe engine and still fulfill the power requirements. Moreover, the engine can beturned off in idle phases for both architectures and for the parallel architecture,it may be turned off whereas the electric motor furnishes all the traction power.The optimal control problem of the energy management between the two powersources is solved for given driving cycles by a classical dynamic programmingmethod and by an alternative method based on Pontryagin Minimum Principle.The real time control laws to be implemented on the vehicle are derived from theresulting optimal control strategies. These control laws are evaluated on anotherdriving cycle which was not given a priori.

Keywords: Hybrid vehicle, Optimal control, Dynamic programming, Pontryagin,Control strategies

1. INTRODUCTION

Growing environmental concerns coupled withconcerns about global crude oil supplies stimu-late research on new vehicle technologies. Hybrid-electric vehicles appear to be one of the mostpromising technologies for reducing fuel consump-tion and pollutant emissions (German, 2003) :mainly thanks to the system stop’n go that allowsto turn off the engine in idle phases, to the recu-perated braking energy to be stored in a batteryand re-used later via the electric motor and to thepossibility to downsize the engine.

The energy management of hybrid power trainsrequires then some specific control laws : they rely

on the estimation of the battery state of chargewhich provides the remaining level of energy, andthe variable efficiency of each element of the powertrain has to be taken into account. Optimizationof energy management strategies on given drivingcycles is often used to derive sub-optimal controllaws to be implemented on the vehicle (see amongothers (Sciarretta et al., 2004), (Scordia, 2004),(Wu et al., 2002), (Delprat, 2002)).

IFP, in partnership with Gaz de France and theAdeme, has combined its downsizing technologywith a natural gas engine in a small urban demon-strator vehicle (VEHGAN vehicle), equipped witha starter alternator and supercapacitor manufac-tured by Valeo (Tilagone and Venturi, 2004).


In this paper, we present two different optimiza-tion algorithms and apply them to a simplifiedmodel of the VEHGAN vehicle and to a parallelarchitecture version of this vehicle: a classical Dy-namic Programming algorithm ((Wu et al., 2002),(Scordia, 2004), (Sciarretta et al., 2004)), and anoriginal algorithm based on Pontryagin MinimumPrinciple that allows to handle constraints on thestate and control variables. Finally, we proposetwo types of control strategies derived from theoptimization results on given driving cycles andevaluate them as a real time strategy on a drivingcycle which was not given a priori.

2. SYSTEM MODELLING AND OPTIMALCONTROL PROBLEM

2.1 Characteristics of the considered hybrid vehicle

Two different architectures are modelled:

• a mild hybrid architecture : the engine cannot be stopped when the requested torque isprovided only by the electric motor, exceptfor the stop’n go mode at the idle speed.So, for a control that cancels the enginetorque and for positive torque request, thefuel consumption does not vanish (Figure 1),

• a full parallel hybrid architecture : the enginecan be stopped to let the electric motorpower alone the vehicle. In that case, the fuelconsumption vanishes.

In both cases, the battery is regenerated in brak-ing phases accordingly to the available minimumelectric torque at the considered engine speed.

In order to solve the optimal control problem ofenergy management, we build a simplified modelwhich is composed of :

• a driving cycle to be followed (imposing ve-hicle speed and gear shifts),

• a vehicle model defining its mass, wheel in-ertia, resistance force,

• a manual gearbox with 5 gear ratios,• a 660CC natural gas engine characterized by

a fuel consumption map displayed in Figure 1and a maximum torque depending on theengine speed (see (5)),

• a starter alternator (3kW for mild-hybrid,6kW for full-hybrid) characterized by a max-imum torque and a minimum torque for re-generative braking phases, both dependingon the engine speed (see (6)). Its efficiency isassumed to be 1 in the presented examples,

• a battery characterized by a capacity of0.4Ah for mild-hybrid architecture and 40Ahfor full-hybrid one. The variations of the bat-tery state of charge are modelled by

1000 2000 3000 4000 5000 60000

10

20

30

40

50

60

70

80

90

0.18

0.18

0.180.18

0.18

0.18

0.19

0.19

0.19

0.19

0.19

0.19

0.2

0.2

0.20.2

0.2

0.2

0.21

0.21

0.210.21

0.21

0.21

0.22

0.22

0.220.22

0.22

0.22

0.23

0.23

0.230.23

0.23

0.23

0.24

0.24

0.24

0.24

0.24

0.24 0.25

0.25

0.25

0.25

0.25

0.26

0.26

0.26

0.260.26

0.27

0.27

0.27

0.270.27

0.28

0.280.28

0.28

0.28

0.28

0.29

0.29

0.29

0.29

0.29

0.29

0.3

0.30.3

0.3

0.3

0.3

0.310.31

0.31

0.310.31

0.32

0.320.32

0.320.32

0.33

0.33

0.33

0.330.33

0.34

0.34

0.340.34

0.34

0.35

0.35

0.350.35

0.35

0.360.36

0.360.36

0.36 0.37

0.370.37

0.370.37 0.38

0.380.380.38

0.38

0.39

0.39

0.390.39

0.39

0.40.40.4

0.40.4

0.41

0.410.41

0.410.41

0.420.420.42

0.420.42

0.430.430.430.43

0.43

0.440.44

0.440.44

0.44

0.45

0.450.450.450.45

Engine Speed (rpm)

Eng

ine

Tor

que

(N.m

)

Fig. 1. Fuel consumption map of natural gasengine of VEHGAN vehicle

x(t) = −ω(t)Tm(t)K ′

Ubattncapa

(1)

with ω(t), the electric motor and enginespeed (assumed to be equal), Ubatt, the bat-tery voltage considered to be constant, K ′,a scaling constant and ncapa, the nominalcapacity of the battery.

The driving cycle is converted in a (engine speed,torque) trajectory either thanks to a backwardmodel based on the vehicle model, or thanks to aforward model as in AMESim Drive library whichfurnishes a more realistic trajectory taking intoaccount a simulated behavior of a driver as theanticipation of the driving cycle.

2.2 Optimal Control Problem

The optimal control problem under study consistsin minimizing the fuel consumption of the vehiclealong a given driving vehicle cycle, taking intoaccount physical constraints from battery, engineand electric motor. The control variable associ-ated with this problem is called u(t). It representsthe distribution of the requested torque Trq, be-tween the engine torque Te and the electric motortorque Tm, written as

Trq(t) = Te(t) + Tm(t)Te(t) = u(t)Trq(t)Tm(t) = (1 − u(t))Trq(t).

(2)

The state variable is the battery state of chargex(t) and follows from (1)

x(t) = −Kω(t)(1 − u(t))Trq(t) = f(u(t), t), (3)

where K = K′

Ubattncapa.

The resulting optimization problem is then thefollowing :


minu

J(u) =

T∫0

L(u(t), t)dt + g(x(T ), T )

subject to : x = f(u(t), t), x(0) = x0

xmin ≤ x(t) ≤ xmax

umin(t) ≤ u(t) ≤ umax(t)

(4)

with 0 and T , respectively the initial and thefinal times of the given driving cycle, L(u(t), t),the instantaneous fuel consumption, computedfrom the map displayed in Figure 1, g(x(T ), T ),the penalization term that constrains the finalstate of charge to be close to the initial state ofcharge in order to maintain a null electrical energybalance (to avoid to discharge totally the batteryfor minimizing the consumption).

The bound constraints on the state and on thecontrol in (4) are derived from the following con-straints :

• the engine can only produce a positivetorque, and is limited to a maximum torquewhich depends on engine speed ω(t), writtenas 0 ≤ Te(t) ≤ Tmax

e (ω(t)), and leads to

0 ≤ u(t)Trq(t) ≤ Tmaxe (ω(t)), (5)

• the electric motor torque is limited betweena maximum torque and a minimum torqueduring regenerating breaking, Tmin

m (ω(t)) ≤

Tm(t) ≤ Tmaxm (ω(t)), and leads to the control

constraints

Tminm (ω(t)) ≤ (1 − u(t))Trq(t) ≤ Tmax

m (ω(t)),(6)

• the storage capacity implies a minimum anda maximum state of charge of the battery(which are fixed to 0% and 100% in ourexample)

xmin ≤ x(t) ≤ xmax. (7)

In this optimal control problem, we make severalassumptions

• the pollutant emissions are not taken intoaccount in the optimization process,

• the engine speed and the electric motor speedare equal,

• in the mild hybrid case, recharging the bat-tery is only possible for negative torques(breaking request), we did not consider re-generation by an additional engine torquebeyond the driver request torque. Thus thecontrol u(t) remains between 0 and 1. In thefull hybrid case, u(t) can take values largerthan 1, allowing battery regeneration withadditional engine torque.

In the following, we will call U(t) in continuoustime (respectively Uk in discrete time) the feasibledomain for u(t) (respectively uk) with respect tothe constraints (5) and (6).

3. DYNAMIC PROGRAMMINGOPTIMIZATION

The Dynamic Programming method (DP) is clas-sically used to solve the problem (4) ((Wu etal., 2002), (Scordia, 2004)) : it relies on the prin-ciple of optimality or Bellman principle. First, theoptimal control problem (4) is discretized in time

minuk∈Uk

J(u) :=N−1∑k=0

Lk(uk) + g(xN )

subject to : xk+1 = fk(xk, uk), x(0) = x0

xmin ≤ xk ≤ xmax

(8)

where Lk(uk) is the cumulated fuel consumptionover the time interval [k, k + 1], xk is the stateof charge of the battery at time k, fk is thefunction that modelizes the battery state of chargeevolution in the discrete form of (3) and g(xN ) =β.(xN − x0)

2 is the penalization term for theconstraint on final state of charge (β is a constantto be chosen 1 ), N being the final time of thedriving cycle.

From Bellman principle, the minimum cost Vk(xk)at the time step k, 0 ≤ k ≤ N − 1, is expressed as

Vk(xk) = minuk∈Uk

(Lk(uk) + Vk+1(fk(uk))). (9)

At time N , the cost function is VN (xN ) = g(xN ).

This optimization problem is solved backwardfrom final time step to initial time step using adiscretization of function V in the control spaceand in the state space.

3.1 DP Optimization algorithm

A standard time step used in our examples is 1s,and the step for state discretization is 0.5%. Twoalgorithms may be used to solve the DP problem :

• a classical DP algorithm, called Ford algo-rithm in the following (Scordia, 2004), con-sists in exploring all the feasible controls (togo from a point xi

k to an other point xjk+1),

finally taking the best trajectory (the trajec-tory which minimizes at each step k the sumLk(uk) + Vk+1(fk(uk))). In such a method,the state of charge trajectory remains on thepoints of the defined grid in the state spacewhich may lead to inaccurate results.

• the chosen algorithm interpolates the func-tion V (xk, k) in the state space, for eachtime step k thanks to an upwind scheme(Guilbaud, 2002) :

1 In the following results, a value depending of battery

capacity has been implemented


0 100 200 300 400 500 600 700 800 900 10000

20

40

60

80

100

Time

Tor

que

(Nm

) an

d S

peed

(m

/s)

Vehicle speed and requested torque

0 100 200 300 400 500 600 700 800 900 1000−20

0

20

40

60

80

100State of charge trajectories

Time (s)

Sta

te o

f cha

rge

(%)

Upwind scheme with dX=2.5% − CPU Time 86sUpwind scheme with dX=0.5% − CPU Time 354sFord algo with dX=2.5% − CPU Time 18sFord algo with dX=0.5% − CPU Time 197sPMP algorithm − CPU Time 3s

Requested torque (Nm)Vehicle speed (m/s)

Fig. 2. Urban Artemis cycle (Top); Optimal state of charge trajectory of VEHGAN vehicle computedwith PMP & DP algorithm (Bottom).

Vk(xik) = min

uk∈Uk

[∆tLk(uk) + Vk+1(xik+1)

+fk(uk)Vk+1(x

ik+1) − Vk+1(x

i−1k+1)

∆x∆t], (10)

where ∆x and ∆t are respectively the stateand the time discretization step size. We referto (Guilbaud, 2002) for some theoretical re-sults on the convergence of this method anderror estimations. Therefore, it is possibleto use a (state) continuous constrained opti-mization algorithm to solve each problem (9)which should furnish more accurate resultsthan Ford algorithm. Nevertheless, this algo-rithm is generally more expensive in terms ofcomputing time.

These two optimization algorithms are only usedwhen Trq > 0 : when the requested torque isnegative, the optimal control uk is completelyknown, as the battery is regenerated as much aspossible, the control uk being constrained by theminimal electric motor torque from (6) and bymaximum SOC from (7).

Optimization results obtained with DP methodare displayed on Figure 2.

4. PONTRYAGIN MINIMUM PRINCIPLEOPTIMIZATION

In this section, we propose an alternative methodto solve the optimal control problem (4). It relies

on the Pontryagin Minimum Principle (PMP)and unlike the DP method does not require anydiscretization scheme.

4.1 Pontryagin Minimum Principle

First we consider the optimization problem (4)and introduce the Hamiltonian function, withoutconsidering state and control constraints

H(u(t), x(t), p(t)) = L(u(t), t) + p(t)x(t). (11)

p(t) is called the co-state of our system. Weassume here that L is a smooth convex functionof u.

The Pontryagin Minimum Principle states thefollowing conditions for the unconstrained optimalcontrol problem :

∂H

∂x= −p and

∂H

∂u= 0. (12)

We refer to (Pontryagin et al., 1974) and (Brysonand Ho, 1975) for further details about PontryaginPrinciple.

4.2 Application

The fuel consumption L(u(t), t) to be minimizedin (4), is defined by a discrete map L(ω, Te), mod-


elled by a 2-order polynomial, which is representedas

L(ω, Te) =2∑

i,j=0

KijωiT j

e , (13)

which allows to model a large variety of enginemaps (Rousseau et al., 2006).

4.2.1. Mild-Hybrid case In the mild-hybrid ve-hicle case, the fuel consumption can not be can-celled. We do not consider the stop and start, aswell as the possibility to power the vehicle onlywith the electric motor.

From (12) and (3) we obtain

p = 0 ⇒ p = constant = p0. (14)

Without any constraint on the state and on thecontrol, the problem of minimizing H can be easilysolved. The minimum fuel consumption is thenreached for u∗ so as

∂H

∂u=

∂L

∂u+ p

∂f

∂u= 0. (15)

The optimal control u∗ can be calculated easily bysolving the equation (15), which depends linearlyon u (thanks to (3) and (13)) . u∗ finally dependson p(t), Trq(t) and ω(t)

u∗(t) = −

2∑i=0

Ki1ω(t)i + p0.K.ω(t)

2

2∑i=0

Ki2ω(t)i.Trq(t)

. (16)

The expression of p0 is obtained by replacingu∗(t) by its expression in the state equation (3),and by integrating this equation in time, betweenTinit and τ , Tinit and τ being respectively theconsidered initial and final times.

4.2.2. Full-Hybrid case With the full-hybridcase, we have to consider the possibility to powerthe vehicle only with the electric motor. Theprevious expression of Hamiltonian becomes un-adapted, as the fuel consumption can be com-pletely cancelled. The fuel consumption functionis then discontinuous

Lfh(ω(t), T e(t)) =

0 if u(t) = 0L(ω(t), T e(t)) if u(t) 6= 0.

(17)

The Hamiltonian, in the only electric motor case(u(t) = 0), is then written

Hm(x(t), p(t)) = p(t)x(t). (18)

The optimal control u∗ must then be written as

u∗ = argmin[H(u(t), x(t), p(t)),Hm(x(t), p(t))].(19)

4.2.3. Handling constraints on control and statevariables The previous section presents thecomputation of the optimal control of the con-tinuous problem in a restricted case where noconstraint is introduced. While control constraintsare generally easily taken into account, handlingthe state constraints in the continuous optimalcontrol problem is cumbersome: several singularcases can be found in (Bryson and Ho, 1975).In our application, we are not able to find ananalytic solution of the optimal control problemwith control constraints : indeed, these constraintsdepends on time and depends on p0 which dependson final SOC (cf. previous section). By an iterativemethod (called algo1 in the following), we cancompute the value of p0 in order to reach thedesired SOC at final time with the control, ex-pression (16), projected on its bound constraints.

(Hartl et al., 1995), (Pontryagin et al., 1974),(Evans, 2000), (Bryson and Ho, 1975), (Guilbaud,2002) have studied the general problem (4) withthe state constraints. In our application, we canshow that p(t) presents discontinuities at thetime steps where the state inequality constraintsare saturated. These time steps are not a prioriknown : this prevents us to solve explicitly thecontinuous optimal control problem with thesestate constraints.

4.2.4. PMP Optimization algorithm Consider-ing the difficulties described in previous section,we propose a heuristic iterative method that al-lows to find a sub-optimal trajectory from theconstrained continuous optimal control problem(4). The proposed algorithm consists in an initial-ization step and 3 steps :

(0) algo1 is applied on the driving cycle [0, T ](see Figure 3 Step 0). The obtained optimaltrajectory violates the state constraints, thefarthest SOC (ie the ”most violated point”)from the bounds being for instance at point(x(tv) = −37%, tv = 818s). The initial timeis called ti, here set to 0.

(1) The SOC at tv is projected on the nearestbound of the feasible state domain (for in-stance, SOC is fixed to xmin = 0 at pointtv).

(2) algo1 is applied again on [ti, tv] (see Figure 3Step 2). If the obtained trajectory still vio-lates the state constraints on [ti, tv], steps 1and 2 are applied again on the farthest SOCfrom the bounds (defining a new point tv).This procedure is repeated until the trajec-tory remains on the feasible domain. Then


0 200 400 600 800 1000 1200−40

−20

0

20

40

60

80

Time

SO

C

Step 0

0 200 400 600 800 1000 12000

20

40

60

80

100 Step 3

Time

SO

C

0 200 400 600 800 1000 12000

20

40

60

80

100Final trajectory

Time

SO

C

0 200 400 600 800 1000 1200−40

−20

0

20

40

60

80

100Step 1 & 2

Time

SO

C

Fig. 3. The proposed algorithm based on Pontrya-gin Minimum Principle.

the last point tv becomes the new initial timeti in step 3.

(3) algo1 is applied on [ti, T ] (see Figure 3 Step3). If the obtained optimal trajectory stillviolates the state constraints, steps 1 and 2are repeated. This sequence is repeated untilwe reach the final step T at the desired finalSOC, without violating the state constraints(Figure 3 bottom right).

4.3 Some optimization results

4.3.1. Mild Hybrid case We can compare thetwo optimization algorithms (DP and PMP) onthe Urban Artemis driving cycle (Andre, 2004),in the mild Hybrid case, on Figure 2. The curvesare very similar; we can notice that smaller is thestate step size, nearer to the PMP curve are theDP curves.

Figure 4 presents the operating points (OP) of theengine obtained with PMP algorithm.

In this vehicle configuration, the state constraintsare active 5 times, giving 6 different values of theLagrange multiplier p(t). We display the six curves(green lines) ∂H

∂Te(p) = 0, which give optimal en-

gine torque, function of engine speed. The engineOP are thus moved toward the green optimalcurves when it is possible: the OP located belowthe curves remain unchanged (no battery regen-eration being possible for positive torque requestsfor mild hybrid) whereas the OP located above aremoved toward the curves by decreasing the enginetorque as much as possible (saturating electricmotor torque constraints).

4.3.2. Full Hybrid case Figure 5 gives optimizedoperating points for the engine and the electricmotor (PMP algorithm is used). In addition to

kinetic energy, we assume that it is possible torecharge the battery by using the engine at betterOP, with an ideal efficiency of 1.

As for mild-hybrid case, the optimal trajectory(continuous green line) gives the optimal operat-ing points of the engine by finding the solution of∂H∂Te

= 0. Thus, many of low torque OP are movedto the optimal trajectory, recharging the batteryby imposing a negative electric motor torque. Asthe full-hybrid configuration allows to turn offthe engine for non-zero vehicle speed (pure elec-tric mode), most of OP associated with enginespeed below 3000 rpm and requested torque below20Nm, lead to turn off the engine (points whereengine torque is zero) : turning off the engineis more efficient than the optimal engine torque(green curve : ∂H

∂Te= 0).

5. REAL-TIME CONTROL

From optimization results on Urban Artemis cy-cle, we derive suboptimal control laws that willbe tested on an other cycle. In this section, theFTP72 cycle has been chosen, for its realism ofurban driving.

Two different control laws will be tested : the firstone, based on Optimization results from Pontrya-gin principle, consists of varying the value of p

regarding to the state of charge, to control u(t),then the electric motor. The reference Lagrangemultiplier value p is the mean of optimal values ofp, obtained on Artemis Urban cycle with off-lineoptimization using PMP algorithm.The second one uses a map of electric motortorque created by the optimization results onUrban Artemis cycle. The electric motor torquefrom the map is then weighted by the state ofcharge of the battery : reduced if the SOC islow, increased if the SOC is high. The obtainedresults are displayed in Table 1. For the mild hy-brid configuration, the suboptimal laws give fuelconsumptions which are close to the optimal one.

Table 1. Fuel Consumption

Consump. Th. Optimal p-control Elec. mot.

(l/100km) veh. control based torq. map

Mild-H. 3.32 3.22 3.23 3.23

(-3,01%) (-2,71%) (-2,71%)

Mild-H with 2.86 2.87 2.88

Stop’n go. (-13,62%) (-13,49%) (-13,33%)

Full-H. 2.70 2.83 2.86

(-18.67%) (-14,76%) (-13,85%)

For the full hybrid architecture, the two controllaws give degraded results compared to optimalresults. Many reasons can explain these differ-ences. First, even if Urban Artemis cycle andFTP72 cycle are both realistic of an urban driv-ing, operating points are very different. While


1000 1500 2000 2500 3000 3500 4000 4500

0

10

20

30

40

50

60

70

80

90

100

110

Engine Speed

Req

uest

Tor

que

Engine operating pointsElectric motor operating pointsRequested operating pointsOptimal operating point lines

Fig. 4. Operating points of engine in Mild-Hybrid mode obtained by PMP algorithm for the urbanArtemis Driving Cycle.

1000 1500 2000 2500 3000 3500 4000 4500−20

0

20

40

60

80

100

Engine speed

Req

uest

ed T

orqu

e

Optimal operating point lineEngine operating pointsElectric motor operating pointsRequested operating points

Fig. 5. Operating points of engine in Full-hybrid mode obtained by PMP algorithm for the urban ArtemisDriving Cycle.


requested operating points of Artemis cycle are al-most uniformly located in the whole engine speedand torque space, all requested operating points ofFTP72 are below ω = 3200 rpm, with a majoritybelow ω = 2000 rpm. The consequence is a un-adapted electric motor map for the second controllaw. Concerning the first control law, the optimalp (obtained with PMP algorithm on FTP72) isquite different from the optimal p obtained forArtemis cycle, leading to degraded results.Nevertheless, the consumption gain remains high :−14.76%.

These results illustrate that several driving cyclesare needed to develop efficient suboptimal controllaws based on p-control or electric motor map.The vehicle speed (related to engine speed by gearratios) could also be taken into account to improvefuel consumption gains.

6. CONCLUSIONS

In this study, we have presented two methodsfor optimal control optimization. The heuristicmethod based on Pontryagin Minimum Principle,well known in the free state constraint case, hasbeen applied successfully to our state constrainedproblem, with very similar results to DynamicProgramming methods and a computation timedivided by 100. Nevertheless, there is currently notheoretical proof to confirm the presented valida-tion results. Moreover, there are some limitationsto this approach, mainly the assumptions on thefuel consumption map, modelled by a smooth con-vex function of control u (2-order polynomial) ;this limitation could lead to a bad approximationof the real fuel consumption for some particularengines.Other degrees of freedom, as the gear-shiftingsequence should also be taken into account inthe optimization problem to improve the fuel con-sumption gain. Reduction of pollutant emissionswill also be studied by considering a second statebased on exhaust temperature.From optimization results are derived two types ofsuboptimal feedback laws based on state of chargemeasurements. These laws give encouraging re-sults even if it needs to be improved in the fullhybrid case.

REFERENCES

Andre, M. (2004). The artemis european driv-ing cycles for measuring car pollutant emis-sions. Science of The Total Environment 334-335, 73–84.

Bryson, E. and Y.C. Ho (1975). Applied OptimalControl. Hemisphere Pub. Corp.

Delprat, S. (2002). Evaluation de strategies decommande pour vehicules hybrides paralleles.PhD thesis. Universite de Valenciennes et duHainaut-Cambresis.

Evans, Lawrence C. (2000). An Introduction ToMathematical Optimal Control Theory. Uni-versity of California Berkeley.

German, J.M. (2003). Hybrid powered vehicles.Society of Automotive Engineers (SAE).

Guilbaud, T. (2002). Methodes numeriques pourla commande optimale. PhD thesis. Univer-site de Paris VI.

Hartl, Richard F., Suresh P. Sethi and Ray-mond G. Vickson (1995). A survey of themaximum principles for optimal control prob-lems with state constraints. SIAM Review.

Pontryagin, L.S., V.G. Boltyanskii, R.V. Gamkre-lidze and E.F. Mishchenko (1974). Theoriemathematique des processus optimaux. Edi-tions Mir moscou.

Rousseau, G., D. Sinoquet and P. Rouchon (2006).Constrained optimization of energy manage-ment for a mild-hybrid vehicle. E-COSM -Rencontres Scientifiques de l’IFP.

Sciarretta, Antonio, Lino Guzzella and MichaelBack (2004). A real-time optimal controlstrategy for parallel hybrid vehicles with on-board estimation of the control parameters.Proceedings of IFAC Symposium on Advancesin Automotive Control AAC04 pp. 502–507.

Scordia, J. (2004). Approche systematique del’optimisation du dimensionnement et del’elaboration de lois de gestion d’energie devehicules hybrides. PhD thesis. UniversiteHenri Poincare - Nancy 1.

Tilagone, R. and S. Venturi (2004). Developmentof natural gas demonstrator based on an ur-ban vehicle with a down-sized turbochargedengine. Oil and Gas Science and Technology59(6), 581–591.

Wu, B., C-C. Lin, Z. Filipi, H. Peng andD. Assanis (2002). Optimization of powermanagement strategies for a hydraulic hy-brid medium truck. Proceeding of the 2002Advanced Vehicle Control Conference, Hi-roshima, Japan.

ACKNOWLEDGMENTS

We would like to thank Gilles Corde, PhilippeMoulin and Antonio Sciarretta for helpful discus-sions and advice at various stages of the elabo-ration of this work. We acknowledge Quang HuyTran for his advice on numerical methods.


PERFORMANCE TESTING OF HYBRID VEHICLES IN BARI DOWNTOWN

L. Mangialardi, L. Soria, N. Caccavo, G. Carbone

Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Bari (IT)

Abstract: The analysis of homologation rules ECE 91/441 and further modifications has

moved the authors of this paper to investigate how a driving cycle taking place in the

realistic traffic conditions of a town could lead to different results in terms of fuel

consumption, when compared to the ones obtained by cars manufacturers in respect of the

standard cycles proposed by the European Standards. By this, two driving cycles have

been considered and experimented in the city of Bari, Italy, one following a urban route,

the other taking place on a suburban track. The experiments have been carried out

utilizing two different Hybrid Electric Vehicles provided by two leading and competing

car Manufacturers. The analysis of those experiments has shown which architecture can

be more suitable for final users, and how far the homologation standards are from reality.

Also the theoretical amount of kinetic energy that could be recovered thanks to this class

of passenger cars has been investigated.

Keywords: HEV, series/parallel hybrid vehicles, ECE 91/441 cycle, regenerative energy,

fuel consumption.

1. ARCHITECTURE OF HYBRID ELECTRIC

VEHICLES

The indication “Hybrid Vehicle” sometimes is not

enough to precisely identify the architecture of the

vehicle under consideration, as behind the same

name many differences are hidden especially

depending on the ‘mission’ of the vehicle. That is

why it is necessary to analyze this various

typologies.

1.1 HEV Components and classification

Before describing the Hybrid Electric Vehicles

(which will be referred to as HEV) classes, it is

necessary to briefly summarize the components that

typically can be found on board of any of these

vehicles.

On all HEV one can always find an internal

combustion engine (ICE), an electric machine (also

called motor), a battery pack, a power converter and

a transmission, that mechanically links engines to

wheels.

The way by which these components match,

generates a different classification of HEV:

- Series Hybrid;

- Parallel Hybrid;

- Series –Parallel Hybrid;

- Complex Hybrid.

The complete panorama of HEV classes is showed in

fig.1 (see Cerami, 2005, Genta, 2000).

Fig. 1. Classification scheme of HEVs

To completely develop the potentiality of HEV it is

necessary to design carefully what is called the

Power Management, that is the control strategy

which determines the management and use of power

sources. Usually this control strategy is operated by a

control unit which can coordinate the hybrid system

to satisfy certain aims such as fuel saving, polluting

emissions reduction and performances optimization

(see Amelia, 2005; Szumanowski, 2000).


Although the Power Management depends on

the vehicle architecture, we can identify some

common characteristics:

1. the electric machine can work as an

electromechanical converter in order to

assure the power flow from batteries to

wheels and vice-versa;

2. batteries can be recharged during

decelerations and/or braking (Regenerative

Braking);

3. it is possible to move the vehicle only by

the electric machine, in order to obtain a

complete Zero Emissions Vehicle (but not

for all the hybrid vehicles);

4. in case of vehicle stop or in other

circumstances, when the driver does not

require power, the thermal engine can be

switched off (Idle Stop Mode), with a

consequent fuel saving and a temporary

interruption of emissions (see Westbrook,

2001).

1.2 The hybrid vehicles utilized for the tests

The HEVs considered for the investigation have been

two cars competing on the European market: the

Toyota Prius and the Honda Civic IMA (Integrated

Motor Assist) (see fig. 2). These two vehicles have a

different architecture (Prius is a series/parallel

hybrid, Civic IMA is a parallel one) but they are

comparable in terms of weight (see Toyota Prius,

Caratteristiche Nuovo Modello, 2003, and Honda,

Gamma Civic’04, 2003).

Fig. 2. The two utilized cars: Toyota Prius (left) and

Honda Civic IMA

As a consequence of the different architecture the

power management is of course different in the two

cases: in the parallel architecture of Honda the motor

only gives an “assist” (overboost effect) when the

driver asks for more torque, whereas in the Toyota

case the motor can work also in synergy with the

combustion engine. In fact on the Toyota Hybrid

System the motor can, under certain conditions,

move the car on its own, creating in this way, a Zero

Emissions Vehicle (ZEV). Moreover the

transmission of the Honda Civic is a classic

mechanical five gears gearbox, while on the Toyota,

torque is transferred to wheels thanks to an epicyclic

gear which is automatically controlled.

2. TESTS

Before getting in production, each car is subjected to

a series of tests aiming to measuring the fuel

consumption and polluting emissions by using

standard procedures as to make the results

comparable.

2.1 ECE Directives

Measurements take place in closed chambers under

controlled atmosphere, where the vehicle is placed on

a “rolling-test bench” which is able to vary the

resistance force and therefore simulate the rolling

resistance of tyres and the aerodynamic drag. The

test is carried out by a driver who continuously

follows the velocity cycle and the gear shift sequence

(shown on a screen) as requested by the European

Standards. The tests are completed with the analysis

of the exhaust gases operated by an instrumentation

downstream the car exhaust pipe. It is interesting to

point out that among the European Countries it exists

a sort of standardization for what concerns the

collection of polluting emissions and the analysis of

the fuel consumption data. But, not the same happens

in the case of the sequences of accelerations, speeds

and gear shifting that has to be followed during the

tests. Nowadays, several standard cycles exist (five

are the most important) which reproduce the average

use of passenger cars in Europe, United States and

Japan. In Europe, at the end of the ‘60s, the

environment and energy saving aspects have lead to

the birth of the international commissions, whose

goal was the monitoring of real traffic conditions in

different urban textures. These commissions

generated a series of judging criteria which gave life

to the European Directive ECE R15-04 which has

been utilized till to a few years ago. The ECE R15-04

cycle was made of an ideal track of 1013 meters to

be repeated four times at the following conditions: (i)

average speed of 18.7 km/h, (ii) maximum speed of

50 km/h and (iii) duration time of engine idling mode

equal to 31% or total running time. Later –in 1993–

in order to take into account also higher vehicle

speeds, the European Ministry Council approved a

new homologation cycle, the ECE 91/441, that

modified the previous one by adding a new piece of

track at higher speed for a total length of 11 km. The

average and maximum speeds in this case became

respectively of 32.5 and 120 km/h. At the same time

more severe restrictions were put on polluting

emission limits, this was the Directive Euro 1.

Directives Euro 2, 3 until 4 follow substantially the

same methodology but imposing more and more

severe restrictions.

2.2 Merits and lacks of the ECE standards

From the given information it is clear that the

homologation directive 91/441 and its further

modifications offers some important advantages:

• fixing the test parameters, they allow a

direct comparability among the

performances of different vehicles operating

in similar conditions;


• the cycle is of great utility in the statistical

study of vehicles reliability in long periods,

offering conditions that are easily

reproducible in industrial environments.

Unfortunately, to this positive notes some evident

limitations are opposed:

o the cycle does not reproduce the real

driving style of an average driver,

especially in metropolitan areas where the

traffic conditions are more severe and the

vehicle is subjected to a higher frequency

of “stop-&-go”;

o the ECE cycle does not follow any realistic

urban topography, it is just an ideal track,

not related at all to the actual traffic

conditions, fuel consumption and polluting

emissions which can be encountered in day

life;

o recorded data on fuel consumption result

fake: in particular they show fuel

consumptions to be better than realistic

values, providing to the user, in this way,

not completely reliable indications;

o the measured emissions – directly

depending on the amount of burnt fuel –

may be altered and, by consequence,

polluting emission values can be higher

than the ones obtained respecting the

European standards.

Because of the aforementioned limitations and due to

the fact that actual standards, having been developed

on the basis of studies of more than forty years ago,

do not provide such realistic consumption values as

to support the final user with reliable information, an

analysis of fuel consumptions in realistic traffic

conditions is needed. The European Community

scientific society does agree with these outlines as

witnessed by the creation of the Artemis cycle – in

many ways similar to the ones realized in this work –

proposed by some research institutes leaded by the

TNO (NL) as a valid alternative to the actual norms

(see TNO Report, 2003).

The traffic conditions under consideration are those

that can be encountered in the city of Bari. The

topography of the city shows an average sidewalk

length shorter than the typical middle European

town (which may be better represented by the ECE

cycle because of their smaller number of stop-&-go),

and closer to that of the southern Europe towns.

2.3 Track choice

In order to have a complete scenario of a driver real

ride, the test was split in two tracks:

1. urban cycle

2. suburban cycle.

As a starting point it was chosen the Dipartimento di

Ingegneria Meccanica e Gestionale (DIMeG),

located in Japigia district in the southern part of the

city. The Urban cycle (also referred to as the slow

test) has been conceived with speeds always lower

than 50 km/h (law limit). From the DIMeG the two

vehicles moved towards the downtown, where

offices and shops are located, drawing a closed ring

track; tests were performed during daytimes, from

8.30 – 9.30 a.m. to 1.00 – 1.30 p.m., when the traffic

conditions are critical. The total length of this track is

of 9 km and 300 meters.

The Suburban cycle (the so called fast test) is,

instead, a route passing close to the city centre

(without entering in it), and later moving (still 50

km/h speed limit) towards the external ring of the

city. Entering the ring the driver keeps an higher

constant speed (90 km/h) which leads him to leave

the ring at the Bari’s southern extreme exit, thus

entering the Japigia district. The length of this track

is of 12 km and 300 meters.

For each car one slow test and one fast were carried

out each day. One day the order was first the slow

test and then the fast one, the day after the inverse

order was followed.

The two tests were characterized by the following

data:

Urban test:

• maximum allowed speed: 50 km/h

• predicted average speed: 18km/h

• predicted maximum number of stops: 42,

split in:

a. stops and priorities: 15

b. traffic lights: 27

• average distance between two stops: 220 m

(approx.)

Suburban test:

• maximum allowed speed:

o 50 km/h inside city walls

o 90 km/h on the ring

• predicted average speeds:

o 18 km/h inside city walls

o 85 km/h on the ring

o 30 km/h globally

• predicted maximum number of stops: 24,

split in:

a. stops and priorities: 6

b. traffic lights: 18

• average distance between two stops:

a. 512 m (approx.) including ring

route,

b. 355 m (approx.) excluding ring

route (that is 3780 m)

Preventive stop number calculations have been made

considering the worst conditions, so considering a

complete vehicle standstill at stops and priorities and

the unfortunate event of always red lamp at traffic

lights.

2.4 Measurement and observation modes

Measurements and checkouts were of two kinds:

a) “on board”

b) “on ground”.

The “on board” ones consisted of data acquisition

using a laptop linked to a GPS with an external

antenna. This allowed the real time recording of the

actual followed routes, thus enabling the calculation


of the partial and total times, effective distances,

instantaneous and average speeds, positive and

negative accelerations, standstill and constant speed

running times. On the Prius, moreover, there was

also the presence of a real time acquisition system

provided by the Manufacturer itself. This, under

constant control of an on board systems operator,

allowed even to collect running times of each driving

unit (ICE and motors), revolution speeds and torque

provided by the motors, ICE revolution speeds and

vehicle speed (this data was later compared with the

one provided by the GPS).

On the Civic IMA the presence of only a

speedometer made more difficult the work of the

operator who had to collect gear shifting and stint

times by the use of an electronic chronometer for

every single test. Duty of the driver was, beyond

driving, the indication of shifting instants and gear

ratio used. Gear shifting had to take place by first

bringing the revolution speed of the combustion

engine to the value of 2200 rpm and then up-shifting

except for the fifth (last) gear, that was engaged until

the ring’s speed limit is reached.

On ground measurements and checkouts were made

in the labs. They consisted of vehicle setups before

tests, and additional data acquisitions. In detail the

following checkouts were performed:

- fuel tank full;

- accumulators charged;

- on board systems switched on and correctly

running;

- air conditioning system switched off;

- car on starting position;

- auxiliary fuel tank weighted;

- refuelling pump weighted;

- (only for Prius) e/v (electric) mode on;

- chronometer present and reset;

- laptop charged and ready;

- GPS antenna positioned and linked;

- (only for Prius) real time acquisition data

system reset and connected;

- (for some sample tests) video-camera

positioned and ready;

- mileage counter reset;

- tyre pressure checked and set.

The fuel tank level check was performed using a

graduated flexible stick. Air conditioning was kept

off in order to avoid the introduction of a disturb

variable in the final consumption data. Starting

position was previously fixed choosing a flat

horizontal zone close to the DIMeG laboratories:

positions of tyres were marked on the ground. Fuel

was refilled using an hand pump which allowed an

accurate control of the amount of liquid provided, an

auxiliary tank of 5 liters was used to this end. A

precision balance was used for weight

measurements. The auxiliary tank was weighted

before and after each refill together with the hand

pump in order to take into account any possible

residual quantity of fuel.

Concerning the fuel, it was always bought from the

same company, Total Italia Spa. The same company

provided official documents declaring specific

weight of gasoline and its origin. Every day the data

concerning the meteorological conditions were

acquired at the DIMeG (humidity, temperature,

pressure, etc.). Before every test tyre pressure was

checked and possibly set using a digital manometer

and an air compressor.

3. RESULTS

A total number of 35 tests were performed using the

two mentioned vehicles; for each test the kinematic

data were collected by the GPS and fuel consumption

data –as said before– by the direct measurement.

3.1 Meteorological conditions

After collecting temperature, relative humidity,

atmospheric pressure and precipitation data, an

attempt was made to find a direct link between

weather conditions and tests duration, as one can

think that a raining event can push more users to

engage the road net. However, measurements showed

that the duration time increased specially during

intense raining but less or even did not increase

during weak phenomena. In fact, there were days

when in spite of a dry weather, particularly long

duration times were recorded. The comparison

between the weather situation and test duration

showed a significant correlation only in suburban

tests case; in urban tests there was no apparent direct

connection. Theoretically this phenomenon can be

explained by observing that the ring traffic is affected

by less variables than the city traffic. The workers or

commuters that have to cover long and middle range

distances will indeed use their cars anyway, either in

case of rain or in case of sun; on the contrary, city

centre traffic is subjected to factors that may be not

only related to meteorological phenomena.

3.2 IMA time

Being a parallel hybrid vehicle, the Honda Civic

internal combustion engine is continuously running

during the ride (except during standstills in “idle stop

mode”). In this case the main data, which were

collected, concerned the motor inserting time, i.e. the

periods of time during which the electric machine

was providing torque (“Assist mode”). The obtained

values are shown in figure 3.

HONDA CIVIC IMA

IMA Assist Time

0

10

20

30

40

50

60

70

80

90

100

[% o

n t

ota

l ti

me

]

URBAN TEST: IMA Time

SUBURBAN TEST: IMA Time

Fig. 3. IMA assist time. Columns show the motor

inserting time in percentage on total tests

duration


One can note that the driving style significantly

influences the motor insertion: in fact, the motor

gives its contribute depending on the torque demand

from the driver: the more intense and longer the

power demand is, the more the insertion lasts. Since

we adopted a soft driving style, the electric assist was

– in terms of time – rather low.

Driving in suburban cycle, of course, required higher

power because of the higher average velocities. This

of course turned out in longer motor insertion times.

3.3 ICE insertion periods

Prius data more carefully analyzed were concerned

with the internal combustion engine running. The

different architecture of the car (series/parallel), in

fact, allowed only a minimum driver’s autonomy in

the choice of which driving unit to use. So, having

given priority to the use of the motor, it came out that

the endothermic engine running time was, in the

series/parallel architecture of the Prius, much less

than in the parallel one of Honda (fig. 4).

Differences in insertion times can be explained

considering that during each experiment, the use of

the electric propulsion was always preferred.

TOYOTA PRIUS

Endothermic Engine Running Times

0

10

20

30

40

50

60

70

80

90

100

[% o

n t

ota

l te

st

tim

e]

URBAN TEST: ICE Running Time

SUBURBAN TEST: ICE RunningTime

Fig. 4. ICE running time shown as a percentage on

total test duration

Now, the electric propulsion is subordinated to the

battery state of charge and the avoiding of 50 km/h

speeding (that is also the road code limit). The

Toyota Power Management, indeed, was such to

insert the ICE when this speed value was exceeded.

In this way the ICE was running only in few

occasions as during the (rare) requests of torque

surplus, and when the low batteries state of charge

was reached. This led to a lower time percentage use

of the internal combustion engine with respect to the

total ride time. On the contrary, in the suburban cycle

– on the ring – where higher average and maximum

speeds, over 50 km/h are imposed, the fully electric

propulsion mode was disengaged and the IC engine

remains substantially always switched on.

3.4 “Stop-&-Go”

As it clearly appears, the coverage time of a test is

heavily influenced by times and durations of stops.

Of course one expects that a urban test has a greater

number of stops and re-starts (“stop-&-go”) than a

comparable length suburban one.

Tests in Bari confirmed this expectation showing an

high number of stop-&-go especially in the city

centre. The stop-&-go time were carefully analyzed

together with the duration time in which the vehicles

travelled at constant speed. This allowed to carry out

a comparison with the standard European cycles.

Concerning stops, the average values were:

• 50 stops per each urban test (9310 m),

equivalent to one stop every 185 metres

approx.

• 28 stops per each suburban test (12300 m),

equivalent to one stop every 440 metres

approx. .

Table 1 shows the percentages on total time during

which the vehicles had no acceleration, that is in

cases of standstills or constant speed motion. Data

are put in comparison with the ones from the

European Directive: one can note that only in the

case of vehicle moving at constant speed in suburban

tests, experimental data are relatively close to the

ones of the European standards. In all the other cases,

the obtained values differ remarkably from reality,

thus supporting the conclusion that real city traffic

possesses features which deeply differs from the

model provided by Community directives.

Table 1. Comparison European Norm/Tests in Bari

Percentages on

total time NEDC Norm

Urban

Tests Suburban Tests

Standstill 33 41 30 Constant

Speed motion 36 26 38

3.5 Speed

The GPS system allowed the monitoring of the

position and velocity of vehicles with relatively good

precision.

Table 2 presents the average speeds recorded during

the execution of tests for both the cars.

Figures 5 and 6 show for comparison an example of

speed trends for a vehicle in suburban test and the

ECE cycle.

Table 2. Tests average speeds

Toyota Prius Honda Civic IMA

Urban Tests

[km/h] 13.15 13.85

Suburban Tests

[km/h] 29.39 27.57

3.6 Acceleration

As previously mentioned, during the execution of the

tests great care was given to avoiding sudden

accelerations. This was accomplished by using a soft

driving style, in order to save as much fuel as

possible.

It is important to underline that during normal

driving, it is not always possible to adopt such a

similar driving style. Thus, the measured

consumption data should be considered as close to

the best obtainable values, that represent an inferior

limit.

After collecting acceleration data, they were

processed and divided in positive and negative


accelerations and subdivided in classes of 0.5 m/s2.

Positive accelerations were put in comparison with

the New European Driving Cycle (NEDC) norm,

negative ones were used to calculate the theoretical

amount of energy that can be regenerated by the

electric machines.

Central Stint Speeds

0

20

40

60

80

100

120

450 550 650 750 850 950 1050

Time [sec]

Sp

eed

[k

m/h

]

Fig. 5. Example of speed trends during a suburban

test

Fig. 6. ECE Cycle: composition of UDC, Urban

Driving Cycle plus EUDC, Extra Urban Driving

Cycle

Concerning the positive accelerations, the

comparison with the European Directive showed a

substantial difference in the distribution of time

percentages: the Norm, in fact, dedicates most of the

time to acceleration classes between 0.5 and 1.0

m/s2, whereas during realistic tests the major amount

of time during which the acceleration was kept into a

certain class fell in the range between 0 and 0.5 m/s2

(see figure 7). Moreover the Norm does not contain

positive accelerations larger than 1.5 m/s2, whereas

in realistic situations they do exist accelerations

beyond this limit. Of course the weight of these is

not prevailing (see figures 8 and 9), but one has to

remember that for intense accelerations and high

RPM number, the endothermic engine goes through

decreasing efficiency conditions, and, by

consequence, faces a worsening of fuel consumption.

Comparison on positive accelerations

0

5

10

15

20

25

30

35

40

45

Acceleration classes [m/sec2]

Tim

e [

%]

Positive AccelerationsSuburban Test

Positive AccelerationsECE Directive

0-0.5

0.5-1

1-1.5

1.5-2 2-2.5 2.5-3

Fig. 7. Comparison between positive accelerations

imposed by the ECE directive and real values

obtained during the suburban test

Urban Tests - Positive Accelerations

0

5

10

15

20

25

30

35

40

Acceleration Classes [m/s2]

tim

e [

%]

0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0

[d.s. 5.0]

[d.s. 0.26]

[d.s. 0.17] [d.s. [d.s. 0.11] [d.s. 0.08]

d.s. = standard deviation

Fig. 8. Amount of percentage time of acceleration

classes for slow test typology

Suburban Tests - Positive Accelerations

0

5

10

15

20

25

30

35

40

Acceleration Classes [m/s2]

Tim

e [

%]

[d.s. 2.2]

[d.s. 0.6]

[d.s. 0.3][d.s. 0.3] [d.s. 0.2] [d.s. 0.03]

0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0

d.s. = standard deviation

Fig. 9. Amount of percentage time of acceleration

classes for slow test typology

3.7 Regenerative energy

An HEV is as more useful as its electric mode

autonomy increases (see Advanced Hybrid Vehicle

Powertrains 2005, 2005). Unfortunately one simple

charge of the batteries is not able to provide a good

autonomy, that is why modern HEVs use a

regenerative process consisting of a partial recovery

of the vehicle’s kinetic energy during decelerations.

This is achieved thanks to the electric machine that is

able to work both as a motor and as a generator.

During braking and/or slowing down, the power

management system switches off both the driving

units and the let the wheels to drag in rotation the

electric machine making it work as a generator, thus

recharging the accumulators.

This operation cannot take place in every conditions,

as long lasting or too intense decelerations could

create such thermal and vibrational stresses (see

Componenti e Sistemi per Veicoli a Trazione

Elettrica, Parte Seconda, 1991) as to damage the

whole system. Moreover, the braking effect of the

generator alone is not enough to stop the vehicle in

emergency conditions.

Data collected were studied by dividing decelerations

in classes of 0.25 m/s2, then it was investigated the

amount of energy that could have been regenerated

per unit of mass, in case the whole vehicle’s kinetic

energy contributed to the regeneration and in case

where a couple of hypothesized threshold limits were

reducing the kinetic regenerable energy. Of course

deceleration values excessive for the hybrid system

survival were excluded from the calculation: in

particular classes with module more than 2.0 m/s2

were ignored. Calculations also excluded


decelerations under a speed threshold of 20 km/h, as

generally the response time and the amounts of

recoverable energy until this value is negligible. In

order to take into account energy losses, a reasonable

value of the efficiency of conversion of about 0.85-

0.90 has to be considered: of course this is an

approximate value as neither Toyota or Honda

provided the actual values. Figures 10 and 11 report

the theoretical amounts of regenerative energy

ordered by deceleration classes, expressed in J/kg;

please note that in each diagram the two vertical

lines identify the threshold limits which guarantee

the aforementioned system integrity. In fact, as the

real physical limits due to the electric machines was

not known, we assumed two different thresholds

related to two different level of acceptable

deceleration intensities.

Regenerative Energy - Urban Tests

0 ÷ -0.25 -0.25 ÷ -0.50-0.50 ÷ -0.75-0.75 ÷ -1.0 -1.0 ÷ -1.25-1.25 ÷ -1.50-1.50 ÷ -1.75-1.75 ÷ -2.0

0

100

200

300

400

500

600

700

800

900

1000

Deceleration classes

[m/sec2]

[J/K

g]

d.s.: 51d.s.: 80

d.s.: 80

d.s.: 53 d.s.: 80

d.s.: 62d.s.: 81

d.s.: 50

Negative

accelerations up

to -1.50 m/sec2

Negative

accelerations up to

-1.0 m/sec2

Negative

accelerations up

to -2.0 m/sec2

Fig. 10. Regenerative energy, slow test

Regenerative Energy - Suburban Tests

-0.25 ÷ -0.50-0.50 ÷ -0.75-0.75 ÷ -1.0 -1.0 ÷ -1.25-1.25 ÷ -1.50-1.50 ÷ -1.75-1.75 ÷ -2.00 ÷ -0.25

0

100

200

300

400

500

600

700

800

900

1000

Deceleration classes

[m/sec2]

[J/k

g] d.s.: 35

d.s.: 120

d.s.: 80

d.s.: 40 d.s.:

118

d.s.: 100

d.s.: 70

d.s.: 65

Negative

accelerations up

to -1.50 m/sec2

Negative

accelerations up to

-1.0 m/sec2

Negative

accelerations up

to -2.0 m/sec2

Fig. 11. Regenerative energy, fast test

3.8 Consumption

HONDA CIVIC IMA

Consumption

Measured

Consumption - Urban

Cycle

Measured

Consumption -

Suburban Cycle

Omologation

Consumption* -

Combined Cycle

Omologation

Consumption* -

Urban Cycle

0

1

2

3

4

5

6

7

8

9

10

[L/1

00

km

]

Urban Cycle Combined/Suburban

Cycle

*: Omologation 1999/100/EC

dev.std: 0.92

dev.std: 0.62

Fig. 12. Honda Civic IMA: comparison

measured/declared consumption

TOYOTA PRIUS - Consumption

Measured

Consumption - Urban

Cycle

Declared**

Consumption - Urban

Cycle

Measured

Consumption -

Suburban Cycle

Declared**

Consumption -

Suburban Cycle

0

1

2

3

4

5

6

7

8

9

10

[L/1

00

km

]

**: by Directive 80/1268/EEC reprised by Directive

1999/100/EC

dev.std: 0.89 dev.std: 0.94

Fig. 13. Toyota Prius: comparison measure/declared

consumption

The experiments showed that the measured fuel

consumptions of the two vehicles are not the same as

declared by the Manufacturers during the

homologation. This, of course shows that

homologations obtained using the actual standards

give not realistic values. The following figures 12

and 13 show the summary of measured fuel

consumptions for both the two hybrid cars, and

compare the urban and the suburban test data with

the ones declared by the car Manufacturers.

In both cases, one can note the measured data are

always larger than the declared ones as also shown in

table 3.

Table 3. Toyota, Honda: deviation percentages

between declared and measured consumption values

Urban

cycle

declared

[l/100km]

Urban

cycle

measured

(average)

[l/100km]

Deviation

%

Comb.ed

cycle

declared

[l/100km]

Suburban

cycle

measured

(average)

[l/100km]

Deviation

%

Toyota Prius 5.0 6.03 +20.6 4.3 5.69 +32.3

Honda Civic IMA 6.0 8.17 +36 4.9 5.69 +42

4. CONCLUSIONS

This work concerned the study and experimental

analysis of two consumption cycles, urban and

suburban, conceived to verify the correspondence of

the ECE 91/441 cycle and its further modifications,

to the real traffic conditions of a vehicle moving in a

metropolitan town as the city of Bari is.

The experimental analysis, moreover, interested two

motorcars belonging to a rapid development and

diffusion category, the hybrid vehicles, which are

driven by the combination of two engines: one is the

IC engine and one an electric machine.

The analysis put in evidence that the vehicle

performances differ as a consequence of the different

architectures adopted on the two cars.

Between the two considered architectures, the Toyota

series/parallel one appears to be the more promising

from the fuel consumption point of view. In the

Honda’s parallel system, instead, the advantages of

the electric motorization are available only when the

driver requires high values of torque and power; so

when a soft driving style is used, the electric motor is

often disengaged.


Regeneration represents a further frontier of

development for HEVs: at the state of the art,

regenerative braking, together with other technical

devices, provides an energetic recovery estimated

around 30% on global consumption by the

Manufacturers. Vibrations and working temperatures

of electric components limit this chance; so it clearly

appears that this energy increase passes through the

functional streamlining of electric machines and their

related components.

Then, the analysis took in consideration the

homologation cycle ECE in its most recent version

Euro 4. In comparison with it we utilized two

realistic cycles in the city of Bari. Results have

evidenced a significant distance between data

obtained by the Manufacturers respecting the

normative, and the ones recorded during the

experimentation. In fact, although during the

experimentation the same acceleration classes of the

norm were respected (assumed as a reference), it was

found out how the single weights differ. In

agreement on this main lines seems to be the whole

European scientific community. Both the hybrid

vehicles showed the validity of their projects and

allowed to underline a deviation in declared

consumption data that in the best event was of the

20% and reached a top of more than 40%, showing,

in this way, all the limitations of the actual European

homologation cycle.

Aknowledgments: the Authors would like to

thank Toyota Motor Italia and Honda Automobili

Italia for having provided the two motorcars and

the Automobile Club d’Italia – Bari that

sponsored the survey.

REFERENCES

Advanced Hybrid Vehicle Powertrains 2005 (2005),

SAE International Publications, SP-1973.

Amelia Cristiano. Modello di Simulazione del

Sistema di Gestione dei Flussi Energetici in un

Veicolo Ibrido di Tipo Serie/Parallelo (2005).

Università degli Studi Di Roma “Tor Vergata”.

Anderman M., Kalhammer F.R., MacArthur D..

Advanced Batteries for Electric Vehicles: An

Assessment of Performance, Cost and

Availability (2000). State of California Air

Resources Board, CA, US.

Cerami Gaetano. Studio di Drivetrain per

Motorscooter Ibridi (2005). Università degli

Studi di Pisa.


Elettrica, Parte Prima: Sorgenti di Energia,

Pubblicazione CRF (1991). Centro Ricerche Fiat.


Elettrica, Parte Seconda: Sistemi di Trazione,

Pubblicazione CRF (1991) Centro Ricerche Fiat.

Crovetto Carlo. Introduzione Alla Trazione Elettrica:

Le Batterie (1997). In Auto Tecnica, Nuovi

Periodici Milanesi, vol. n.6, Milan.

Electric Vehicle Batteries R&D (2001). U.S.

Department of Energy, Energy Efficiency and

Renewable Energy, Office of Transportation

Technologies. Annual Progress Report. USA.

Evaluation of the Environmental Impact of Modern

Cars on Petrol, Diesel, Automotive LPG and

CNG (2003). TNO Report,

03.OR.VM.055.1/PHE.

Gabriel Martin G.. Innovations in Automotive

Transmission Engineering, SAE International

Publications, T - 109, 2004.

Genta Giancarlo. Meccanica dell’Autoveicolo

(2000). Levrotto & Bella, Torino.

Honda, Gamma Civic ’04 (2003). Cartella Stampa

Honda, Verona.

Szumanowski Antoni. Fundamentals of Hybrid

Vehicle Drives (2000). Warsaw-Radom 2000.

Toyota Prius,Caratteristiche Nuovo Modello (2003).

Serie NHW20, Toyota Motor Publication,

NCF256IT.

Westbrook Michael H.. The Electric and Hybrid

Electric Car (2001). SAE International

Publications.


HYBRID VEHICLES WITH ELECTRICAL MULTI ENERGY UNITS

M. Cacciato, A. Consoli, G. Scarcella, A. Testa

Department of Electrical, Electronics and System Engineering Viale Andrea Doria, 6 - 95125

Catania, Italy

Abstract: In order to evaluate electrical and hybrid vehicles performance, mathematical models of SCs, FCs, and PV modules have been implemented in Advanced Vehicle Simulator. A deep analysis about the advantages of integrate standard batteries with new storage devices, as super-capacitors, fuel-cells and photo-voltaic modules has been done. For each electrical units described above, an accurate balance has been done. Moreover, using a multi-criteria approach a cost-benefit analysis has been performed considering in a period of ten years, in order to evaluate the economical advantages of using the additional units. Keywords: Super-capacitors, photo-voltaic modules, ADVISOR, cost-benefit analysis.

1. INTRODUCTION

In the last years, the global request of energy has increased at high rate and the forecasts for the next future guess a faster rate of growing in the energy demand. As a consequence, many environmental problems has been experienced related with the high percentage of Carbon Oxide (CO), Nitrogen Oxides (NOx), subtle dusts, etc., present in the atmosphere. Such a problems are more relevant in urban areas because of high density of population and, consequently, of the use of polluting devices. In particular, in the last years an enormous increasing of the pollution has been experienced due to the rising number of vehicles. On the other hand, conventional energy sources, as petroleum, are expected to be exhausted in some tens or, at most, few hundreds of years. Considering such a scenario, it is essential to develop ‘clear’ and highly efficient vehicles, such as electrical ones, ‘pure’ or ‘hybrid, that allow to reach high performance, similarly to those of internal combustion engine, while using clean energies. In order to increase the performance of electrical and hybrid vehicles, enabling technologies are Super-Capacitors (SCs), Fuel Cells (FCs) and Photo Voltaic (PV) modules, that can be integrated in hybrid and electrical vehicles. To evaluate the vehicles performance, mathematical models of SCs, FCs, and PV modules have been implemented in Advanced Vehicle Simulator (ADVISOR), developed by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy. The ADVISOR is a very flexible tool, implemented in Matlab, that enables fast and accurate performance analysis and to calculate fuel savings of conventional and advanced, light and heavy-duty vehicles, as well as hybrid electric and fuel cell vehicles (A. Brooker et al., 2002). Using such a tool, a deep analysis has

been done for two vehicles, a car and a bus. Moreover, the cost of different solutions has been considered to evaluate their impact on the vehicles economy.

2. ADVISOR MODELS In order to investigate the impact on FC vehicles performance of new electrical units as SCs and PV modules installed on board, the model of two electrical vehicles, powered by a FC, has been used. To this aim, new models of SCs and PV modules have been developed in Matlab/Simulink and implemented in such a way to be integrated in the ADVISOR environment. The great flexibility of such an approach, allows to easily evaluate many vehicle configurations in different situations and to easily compare the results (A. Emadi et al., 2004). 2.1 Car model. As a reference car, an electrical Mercedes-Benz F-Cell, has been used. Such vehicle is the electrical powered version of standard Class A car, equipped with a fuel cell and a small battery pack to support the fast power transient during the quick accelerations. The main vehicle specifications are reported in Tab. 1 (M. C Pera et al., 2002).

Tab. 1: Main parameters of F-Cell car. Length [m] 3,838

Width [m] 1,764

Height [m] 1,593 Car

Curb weight [kg] 1509


Technology PEM

Voltage [V] 250-450

Pressure [bar] 350

Power [kW] 72

Fuel Cell

Weight [kg] 274

Technology InductionMachine

Power [kW] 65

Efficiency 0.94

Maximum current [A] 384

Minimum voltage [V] 200

Electrical Motor

Weight [kg] 86

Technology Ni-Mh

Voltage [V] 150-250

Power [kW] 15-20

# of mudules 25

Module capacity [Ah] 45

Battery

Total weight [kg] 156

Number of gears 1

Gear ratio 9.9 Transmission

Weight [kg] 108 2.2 Bus model. As a reference bus, the electrical Mercedes-Benz Citaro, has been used. Such vehicle is electrical powered and equipped with a fuel cell. The main vehicle specifications are reported in Tab. 2.

Tab. 2: Main parameters of Citaro bus. Length [m] 11,95

Width [m] 2,55

Height [m] 3,69

Curb weight [kg] 18.000

Bus

Max load [kg] 4900

Producer/Mod. Ballard

Mark902 Technology PEM

Voltage [V] 760

Current [A] 510

Power [kW] 280

Fuel Cell

Weight [kg] 238

Technology Induction Machine

Power [kW] 187

Efficiency 0.95

Maximum current [A] 540

Minimum voltage [V] 400

Motor

Weight [kg] 91

Battery Technology Pb

Voltage [V] 700

Power [kW] 80

# of mudules 66

Module capacity [Ah] 40


Producer/Mod. ZF / HP502C6

Number of gears 6

Gear ratio 3,43 2,01 1,42 1,0 0,83 0,59

Transmission

Weight [kg] 305 2.3 PV roof. It is considered the possibility to integrate a PV generation system in the roofs of the car and bus. For the car, it is considered to built a PV roof suitably designed using single PV cells, while for the bus standard PV modules have been considered. The PV roofs parameters are reported in Tab.s 3 and 4, at a radiance of 1000 W/m2 and 25 °C.

Tab. 3: Parameters of car PV roof. Technology Thin film

Voltage @ open circuit [V] 0,68

Current @ short circuit [A] 0,016

Peak power [mW] 8,5

Cell area [mm2] 45

Cell length [mm] 6,5

Cell weight [g] 0,23

Roof fill factor 0,79

# of cells in series for string 265

# of strings in parallel 162


Tab. 4: Parameters of bus PV roof. Technology Single crystalline

Module length [m] 0,66 Module width [m] 1,48

Module area [m2] 0,98

Module weight [kg] 11,9 # of modules for string 8 # of strings 3 Total weight [kg] 286 Total area [m2] 23,52 Voltage @ open circuit [V] 21,3 Current @ short circuit [A] 8,1 Roof fill factor 0,752 Module peak power [W] 130


2.4 Super capacitors. Nowadays, SCs are an emerging class of passive devices, able to store relevant energy quantities while working at high power levels. The SCs are derived from standard electrolytic capacitors largely used in power electronic applications which are able to operate at high power, in addiction, SCs show a very high capacitance value per volume, up to one hundred time the electrolytic capacitors (Barker P. , 2002). In high efficiency vehicles, the regenerative braking is highly desirable, but, the batteries can not be recharged at the power level of braking, that can reach the nominal power of the electrical machine. Therefore, two technical solutions are possible, the former consists in a partial recovery of the available energy during the braking, because of the limited power that can recharge the batteries. The latter, using a energy buffer like SCs, allows the full recovery of the breaking energy. The last solution, although energetically efficient, is more expensive because of the actual high price of SCs and the need of an auxiliary power converter for controlling the power flowing trough SCs. The characteristics of SCs, as reported in Tab.5, well match the requirements of automotive applications. The benefits obtainable using such components have been evaluated.

Tab. 5: Parameters of single SC and SC bank. Voltage @ open circuit [V] 2,4

SC weight [g] 15

ESRd [mΩ] 12,6

Energy density [Wh/kg] 6,1

Power density [W/kg] 3500

# of SCs in a bank 196

Nominal bank voltage[V] 450

Bank power [kW] 10

Total weight [kg] 2,86 2.5 Test cycles. The test cycles used to evaluate the vehicles

performances are obtained as a combination of standard cycles and stop periods. The cycle used to test the F-Cell car is constituted by two ECE speed profiles, a stop period and a standard EUDC speed profile, as reported in fig. 1.

Fig. 1. Used test cycle used for F-Cell car. The elevation is introduced as a parameter. The cycle used to test the Citaro bus is constituted by two groups of, respectively, seven and five ECE speed profiles, split by a stop period, as reported in fig. 2.

Fig. 2. Used test cycle for Citaro Bus.

3. VEHICLES PERFORMANCE EVALUATION 3.1 F-Cell Car. Considering the test cycle reported in fig. 1, the following F-Cell car configurations have been simulated:

• WES without energy storage systems • CB with batteries • UC with super capacitor banks only • CBUC with batteries and super capacitor banks

Fig. 3. Matlab scheme of the vehicles with FC, batteries, PV and SC units.


The first configuration (WES) consists in the F-Cell car without any batteries, then, the energy recovery during the braking operation is not allowed. The second configuration (CB) uses standard batteries. The third configuration (UC), only uses a SCs bank, while the last configuration exploits both storage systems, opportunely sized. In Tab. 6, are reported the car weights in correspondence of each configuration.

Tab. 6: Gross weight of F-Cell car for different configurations [kg].

Car Config.

with PV roof [kg]

without PV roof [kg]

WES 1363 1373 CB 1519 1529 UC 1375 1385 CBUC 1462 1472

In Tab. 7, are reported the simulation results for the car. For taking into consideration the initial State Of Charge (SOC) of the storage systems, one or two letters (xx) are used, indicating the SOC of each storage system as follows:

• S SOC high (≥ 0,8) • s SOC low (≤ 0,6)

In red are stressed the results of the worse performance, while in green the best ones. As can be noted, the PV roof considerably reduces the fuel consumption, while the car dynamic performance slightly worsens because of the weight increasing. 3.2 Citaro bus. Similarly for F-Cell car, some configurations of Citaro bus have been simulated considering two battery technologies:

• WES without energy storage systems

• CBPb with lead-acid batteries • CBNiMh with NiMh batteries • UC with super capacitor banks only • CBUCPb with lead-acid batteries and SC banks • CBUCNiMh with NiMh batteries and SC banks

In Tab. 8, are reported the bus weights for each configuration. In Tab. 9, are shown some of the simulation result obtained for each configurations and different SOCs of each storage system.

Tab. 8: Gross weight of Citaro bus for different configurations.

Bus Config.

with PV roof [kg]

without PV roof [kg]

WES 18.000 18.286 CBPb 18.800 19.086 CBNiMh 18.277 18.571 UC 18.020 18.306 CBUCPb 18.508 18.794 CBUCNiMh 18.285 18.571

It is noticeable that, using a PV roof, the fuel saving is higher with respect to the cases of the car because of the large extent of the bus roof.

4. COST-BENEFIT ANALYSIS For each electrical units described above, an accurate balance has been done, taking into account the energy saved or recovered by the units and power losses due to each unit efficiency and the increment of the vehicle weight. Such energy balance is evaluated for the F-Cell car supposing a journal trip of 2, 8 hours per day, corresponding to a route of 65 km, obtained combining some standard cycles. For the Citaro bus, a daily duty of 16 hours, corresponding to a route of 250,5 km has been considered.

Tab. 7: Parameters of single SC and SC bank.

without PV roof with PV roof Car

Config. H2 [litres]

Equivalent Fuel [litres]

Acceleration0-100 km/h

Max speed

H2 [litres]

Equivalent Fuel [litres]

Acceleration 0-100 km/h

Max speed

WES 83,9 5,7 17,7 153,8 79,9 5,4 17,7 154,1

CB S 32,1 2,2 15,3 154,2 31,2 2,1 15,4 154,2

CB s 102,9 7,0 19,8 152,5 98,1 6,6 19,8 152,7

UC S 80,2 5,1 14,0 154,0 71,0 4,8 14,1 154,3

UC s 80,0 5,4 17,8 153,7 75,5 5,1 17,9 154,0

CBUC SS 57,9 3,9 14,8 154,7 53,2 3,6 14,9 154,7

CBUC Ss 60,2 4,1 15,7 154,7 55,2 3,7 15,7 154,7

CBUC sS 89,1 6,0 17,2 153.2 83,7 5,7 17,1 153,6

CBUC ss 91,2 6,2 19,0 153,0 86,2 5,8 19,0 153,3


Tab. 9: Parameters of single SC and SC bank.

SOC Consumption [l/100 km]

Acc. 0-50 km/h [s]

Max speed [km/h] Bus

Config. Batt. SC without

PV roof with PV

roof without PV roof

with PV roof

without PV roof

with PV roof

WES // // 1172,0 1119,9 14,7 14,7 81,9 81,9

0,62 // 1158,8 1109,7 15,4 15,4 82,0 82,0 CBPb

0,75 // 1049,2 999,8 12,3 12,5 81,8 81,8

0,62 // 1136,7 903,6 15,1 14,4 82,0 81,9 CBNiMh

0,75 // 1031,0 819,0 12,0 13,1 81,8 81,8

// 0,55 1112,3 861,8 14,7 13,9 81,9 81,9 UC

// 0,75 1107,3 857,3 13,3 12,5 81,9 81,9

0,62 0,55 1128,8 1077,5 15,1 15,1 81,9 82,0

0,62 0,75 1125,2 1073,9 13,4 13,4 82,0 82,0

0,75 0,55 1066,4 1015,4 12,3 12,4 81,8 81,8 CBUCPb

0,75 0,75 1063,6 1012,6 12,1 12,3 81,8 81,8

0,62 0,55 1117,7 1065,0 14,9 14,9 81,9 81,9

0,62 0,75 1113,9 1061,1 13,1 13,2 82,0 81,9

0,75 0,55 1050,1 997,8 12,1 12,3 81,8 81,8 CBUCNiMh

0,75 0,75 1046,6 994,3 11,9 12,1 81,8 81,8

Moreover, taking into account the prices of the fuel and units, with a multi-criteria approach a cost-benefit analysis has been performed, to evaluate the economical advantages of using the additional units in a period of ten years (Chiodo E., 2005). The adopted criteria are max speed, max acceleration, units cost, fuel cost. The algorithm has been implemented in Matlab as a tool of the ADVISOR. In Tab. 10, are reported the

costs used in the cost analysis, the cost of the fuel cell is considered as desired in the next future.

Tab. 10: Costs of electrical units. Pb batteries 100 €/kWh NiMh batteries 300 €/kWh SC 80 €/kW PV 5,4 € / Wp

Tab. 11: Results of the MC analysis for the F-Cell car.

Car config. Accel. 0-100 km/h [s]

Max speed [km/h]

Electrical units costs [€]

Savings [€] Score

WES 17,3 155,0 0,00 0,00 0,0874 WESFV 17,5 155,0 4.284,00 -1.019,00 0,0632 CBNiMh 14,6 154,0 5.883,00 -2.478,00 0,0233 CBNiMiPV 14,8 154,0 7.287,00 -3.392,00 0,0016 UC 15,4 155,0 4.880,00 -1.405,00 0,0501 UCPV 15,6 155,0 6.284,00 -2.319,00 0,0283 CBUCNiMh 14,1 154,8 5.476,00 -1.931,00 0,0353 CBUCNiMhPV 14,3 154,9 6.880,00 -2.775,00 0,0151


Tab. 12: Results of the MC analysis for the Citaro bus.

Bus config. Accel. 0-100 km/h [s]

Max speed [km/h]

Electrical units costs [€]

Savings [€] Score

WES 12,30 81,90 0,00 0,00 0,0586

WESPV 12,50 81,90 18.500,00 -1.700,00 0,0536

CBPb 10,90 81,70 21.000,00 -700,00 0,0525

CBPbPV 11,10 81,80 39.500,00 -10.800,00 0,0323

CBNiMh 10,60 81,70 9.500,00 26.900,00 0,1034

CBNiMhPV 10,80 81,70 28.000,00 23.800,00 0,0959

UC 11,20 81,80 6.400,00 31.050,00 0,1123

UCPV 11,40 81,90 24.900,00 31.100,00 0,1105

CBUCPb 10,70 81,80 15.525,00 18.075,00 0,0869

CBUCPbPV 10,80 81,80 34.025,00 15.325,00 0,0798

CBUCNiMh 10,50 81,80 8.000,00 25.600,00 0,1011

CBUCNiMhPV 10,70 81,80 26.500,00 33.350,00 0,1132

As it is reported in Tab.s 11 and 12, the cost-benefit analysis shows that, for fuel cell car there are no economical advantages in introducing additional power units, while for the bus it is convenient to use NiMh batteries instead of led-acid ones, SC banks and PV roof.

5. CONCLUSIONS In the last years, a relevant increasing of the pollution has been experienced due to the rising number of vehicles. It is essential to develop ‘clear’ and highly efficient vehicles, such as electrical ones, that, at the same time, show performance close to that of internal combustion engine. New technologies as fuel cells, super-capacitors and photo-voltaic modules are now available to increasing the performance of electrical and hybrid vehicles. In this paper, energy and economical evaluations of vehicles performance using those components have been done. To this purpose, mathematical models of SCs, FCs, and PV modules have been implemented in Matlab and integrated in the Advanced Vehicle Simulator, obtaining a very flexible and accurate analysis tool. Using such a

simulator different solutions have been evaluated and interesting results have been obtained and reported.

REFERENCES Brooker, A.; Hendricks, T.; Markel, T.; Johnson, V.;

Kelly, K.; Kramer, B.; O'Keefe, M.; Sprik, S.; Wipke, K. (2002). ADVISOR: A Systems Analysis Tool for Advanced Vehicle Modeling Journal of Power Sources, Volume 110, Issue 2 , 22 August 2002, Pages 255-266.

Emadi, A.; Ehsani, M.; Miller, J. M. (2004). Vehicular Electric Power Systems, Marcel Dekker Inc, New York.

Pera M. C., Hissel D., Kauffmann J. M. (2002) Fuel cell systems for electrical vehicles, IEEE 55th Vehicular Technology Conference (VTC), 6-9 May 2002, vol. 4 , pp. 2097 – 2102.

Barker P. (2002) Ultracapacitors for use in power quality and distributed resource applications, IEEE 2002 Power Engineering Society Summer Meeting, 21-25 July 2002 pp. 316 – 320.

Chiodo E. (1991). Strumenti di supporto alle decisioni per la tecnologia e l'ambiente: analisi multicriteriale deterministica applicata al progetto dei veicoli elettrici, Manutenzione - Tecnica e Management.


IMPEDANCE MATCHING FOR PV GENERATOR

Angel Cid-Pastor1,3, Luis Martínez-Salamero2, Corinne Alonso1, Guy Schweitz3 and Ramon Leyva2 1 LAAS-CNRS, Laboratoire d’Analyse et des Architectures des Systèmes, Toulouse, France

2 ETSE Universitat Rovira i Virgili / Dept. Eng. Electrònica, Elèctrica i Automàtica, Tarragona, Spain 3 EDF R&D / LME Department, Moret sur Loing, France

Abstract.- A comparative analysis between a DC power transformer and a DC power gyrator on equal bases of operation is presented. Both approaches are used to solve the problem of maximum power transference from a PV panel to a DC load. An outdoor measurements system has been implemented and comparative experiments have been carried out during six hours. Results show that both approaches are practically equivalent in terms of efficiency.

I. INTRODUCTION Impedance matching in power electronics basically

means solving the problem of maximum power transfer between a dc generator and a dc load. In particular, the maximum power transfer from a photovoltaic panel to a dc load is an important technological problem in many practical cases dealing with the optimization of a PV conversion chain.

Although there are many works devoted to the problem of the maximum power point tracking (MPPT) in a PV array, only few of them deal with the nature of the power interface while most of them focus on different types of tracking algorithms. The problem of finding the most appropriate power interface is discussed next. The main antecedents in the study of matching power interfaces can be found in the works of Singer and Braunstein on the coupling of a PV array and a dc load by means of a dc transformer with variable transformer ratio [1]-[2].

In this paper, we will study the impedance matching for the maximum power point tracking (MPPT) in photovoltaic arrays using power gyrators. It will be demonstrated that both G-gyrators with either controlled input or output current can be used to solve the MPPT problem with similar efficiency to that of conventional solutions based on the DC-transformer approach.

We will first analyze the matching problem using the notion of a dc transformer and subsequently we will demonstrate that such problem can be solved by using a power gyrator. We will compare, by means of an outdoor test [3], the performances of both systems during 6 hours of measurements.

The outline of the paper is as follows. Impedance matching by means of DC transformer is presented in Section II. In Section III, impedance matching by means of DC power gyrator is analyzed. An outdoor test for efficiency evaluation of both systems is presented in Section IV. A concluding discussion is given in Section V.

II. IMPEDANCE MATCHING BY MEANS OF A DC POWER TRANSFORMER

A. Static operating point of the PV array A DC-to-DC switching converter can be modeled

according to Middlebrook’s paradigm as an ideal DC transformer whose the transformer ratio n(D) is a function of the duty cycle. The connection of the PV generator and the load using a switching converter as interface is shown in Fig.1 where both generator and load have been modeled by a first quadrant v-i characteristic.

PV

V1

I1 +

-

I2

V2 +

-

VOLTAGE-TO-VOLTAGE DC-TO-DC

SWITCHING CONVERTER

LOAD

v

i

v

i

RL fo(i)

VB +

-

Fig. 1. Matching a PV generator to a DC load using a voltage-to-voltage DC-to-DC switching converter

The behavior of the converter in steady-state can be described by means of the following equations

12

12

)(1

)(

IDn

I

VDnV

=

= (1)

which define a DC ideal transformer. The DC load can be modeled by means of the following

function v = f( i )

iRVifv LBo +== )( (2)

with VB > 0 and RL > 0. which corresponds to the Thevenin equivalent of the

usual DC loads supplied by a PV generator. Namely, storage batteries, permanent magnet DC motor, shunt DC motor, electrolysis pool, etc.

From (1) and (2) the following function v1 = fin(i1) is derived

1222

11)()()()()(

)( IDn

RDn

VDnIR

DnV

DnV

ifv LBLBin +=+===

(3)

If we consider that the load is a battery with a very small equivalent series resistance (RL→0), expression (3) becomes


)()( 11 Dn

Vifv B

in ≈= (4)

Fig 2 shows the intersection of characteristics fo and fin with the PV curve under different hypotheses. In this case, the direct connection of the load to the panel would correspond to an operating point (VB) where the output current of the PV generator is zero. As a matter of fact, the value of the voltage battery is greater than the open circuit voltage of the PV generator. It can be deduced from (4) that the characteristics fin will be placed below fo if n(D) > 1. Therefore, from (4) the intersection point A could be placed at left side of M for a certain value of duty cycle D1. On the other hand, the intersection point B corresponds to a duty cycle D2 > D1 since n(D) is an increasing monotonous function of the duty cycle D [4].

The objective of the converter is to achieve a finop characteristic so that it intersects with PV curve at the optimal operating point M.

v

i

M

B

A VOC

ISC

fo

fin(D1)

finopt

fin(D2)

VB

Fig. 2. PV Array operating points ( n(D) >1, D2 > D1)

v

i

M

B

A

VOC

ISC

C

fo

fin(D2)finopt

fin(D1)

Fig. 3. PV array operating points ( n(D) < 1, D2 < D1)

Similarly, figure 3 illustrates the case of an operating point corresponding to a direct connection (point A) which is located at the right of M. In this case, it is mandatory to perform the matching with a n(D) < 1. Note that it can be deduced from (4) that the characteristics fin will be placed above fo if n(D) < 1. Therefore, from (4) the intersection point C could be placed at left side of M for a certain value of duty cycle D2. On the other hand, the intersection point B corresponds to a duty cycle D2 < D1.

The election of converter structure will imply a restriction in the values of n(D). Therefore, we obtain values of n(D) < 1 with a buck converter, values of n(D) > 1 with the boost converter and both of them with the Cuk converter. However, the Cuk converter imposes a sign inversion at the output port.

B. Operating point trajectory of the PV array Now, we will analyze the influence of the duty cycle

variations in equation (4) in order to study the trajectories that allow the displacement of the operating point along the v-i characteristic curve of the PV array.

Therefore

0)(

)(21 <−=

dDDdn

Dn

VdDdV B (5)

since 0))((>

dDDnd in any converter [4] and we

assume n(D) > 0.

On the other hand, we can write

DdDdV

V ∆=∆ 11 (6)

Therefore, we can conclude that increasing the duty cycle will produce a trajectory to the right along the v-i curve (∆ V1 negative), while decreasing D will result in a trajectory to the left along the v-i curve irrespective of the step-up or step-down nature of the converter.

C. Experimental Verification It has been recently demonstrated that an extremum

seeking algorithm was stable in the sense of Lyapunov and that it could applied to the maximum power point tracking of a PV generator by using a voltage to voltage dc-to-dc switching converter in PWM operation [5]. The circuit performing the MPPT control is illustrated in Fig. 4.

Analog

Multiplier DifferentiatorHysteretic

comparator

Flip-flop + Inhibition

delay Integrator

iSA

vSA vC

Fig. 4. Realization of the MPPT controller

The PV panel is a solar array of monocrystalline cells with an open circuit voltage of 22.1 V and a nominal voltage value at the maximum power point of 18 V. Since the load is a 24 V acid-lead battery, the dc-to-dc conversion structure must be performed by a boost structure. Fig.5 shows the practical implementation of a boost dc-to-dc voltage transformer-based with MPPT function. The boost parameters are given by L1 = 75 µH, C1= 12 µF, C2 = 20 µF, V2= 24 V and a constant switching frequency of 150 kHz.


Fig. 5. Practical implementation of a boost converter performing the MPPT of a PV array

Next, it will be shown the experimental behavior of the Is, Vs, Ps of the PV generator and also the duty cycle of the boost converter with the extremum-seeking control algorithm under different operating conditions. Fig. 6.a shows the PV system response after the connection of an additional panel in parallel with the PV generator. As it can be expected, the current increases while the voltage remains practically unchanged except in the transient-state connection. Since the voltage operating point has not changed, the maximum power point is almost instantaneously reached. A similar situation is observed in Fig. 6.b in which the panel previously added is removed.

a)

b)

Fig. 6. Response to a parallel connection and removal of an additional panel (Time scale: 10 ms/div).

III. IMPEDANCE MATCHING BY MEANS OF A DC POWER GYRATOR

A. Static operating point of the PV array If the voltage to voltage dc-to-dc switching converter of

Fig. 1 is substituted by a voltage to current dc-to-dc switching converter, i.e., a G-power gyrator [6], the steady-state equations at both input and output ports of the converter will be given by

12

21gVIgVI

==

(7)

where g is the gyrator conductance. From (2) and (7), we conclude that the input

characteristics iin = fin (v1) will be expressed as

( ) 12

2211 )( VRggVIRVggVVfI LBLBin +=+=== (8)

Considering that the load is a battery with an equivalent series resistance RL→0 the expression (8) becomes

Bni gVfI ≈=1 (9)

Expression (9) shows that the input current will be proportional to the battery voltage with a proportionality factor g (the gyrator conductance).

Figs. 7 and 8 show the intersection of characteristics fo and fin with the PV curve in similar situations as those illustrated in figs. 2 and 3 respectively. Fig. 7 describes the direct connection of the load and the PV array resulting in an operating point located at the left of the maximum power point. It can be derived from (9) that the intersection point B can be placed at the right side of M by an appropriate choice of conductance G (a value of the gyrator conductance g). If we assume that the intersection at point B corresponds to a certain value G1 of the gyrator conductance, then intersection at A will correspond to a value G2 < G1 as derived from (9).

v

i

M

B

AVOC

ISC

fo fin(G2) finopt fin(G1)

VB

Fig. 7. PV array operating points. Impedance matching by means of a G-gyrator (fo(i2) intersects at the left side of M)

Fig. 8, in turn, illustrates the case of a direct connection at point C, which is located at the right side of point M. By an appropriate selection of the gyrator conductance (G = G2) the operating point can be placed at the left side of M (point A). Increasing the conductance value to G1 (G1 >

vC

PS

VS

IS

vC

PS

VS

IS


G2) will establish the operating point at point B, which is located at the right side of M.

v

i

M

B

A VOC

ISC

fo

fin(G2) finopt fin(G1) VB

C

Fig. 8. PV array operating points. Impedance matching by means of a G-gyrator. (fo(i2) intersects at the right side of M)

B. Operating point trajectory of the PV array Now, we will study the influence of conductance g

variations in equation (9) in order to study the trajectories or the operating point along the v-i curve. Hence,

01 >= BVdgdI

(10)

Therefore, we can conclude that increasing the gyrator conductance will result in a trajectory towards the right (∆ I1 positive), while decreasing g will result in a trajectory to the left along the v-i curve.

C. Experimental Verification In [6, 7, 8], different types of power gyrators have been

synthesized and classified. Fig. 9 shows the block diagram of a power gyrator of type G with MPPT function. In order to compare in the same conditions the DC power transformer of section II with the DC power gyrator we have selected the same converter structure to implement a power gyrator, i.e., the boost converter. The boost converter has a pulsating output current, therefore according to the definition of power gyrator gave in [7], the use of a boost converter leads to a power semigyrator implementation.

G GYRATOR

I1 = gV2 I2 = gV1

PV Array

Module MPPT Control

Battery

iSA vSA

g

vSA = v1

iSA = i1

+

-

i2

v2 +

- 24 V

Fig. 9. Block diagram of a MPPT of a PV array based on a power gyrator of type G.

In this case, we would synthesize a G-gyrator intending to transform a voltage source at the output port into a

current source at the input port. Since the regulator establishes the gyrator characteristics through the control of current i1, we will call this class of circuits G-gyrators with controlled input current [6]. Hence, we impose a sliding mode surface S(x) = gV2 - i1, where V2 is a constant voltage.

The analysis of the sliding-mode induced by considering S(x) = gV2 - i1 results in a stable equilibrium point for the boost converter, the characteristic equation being of zero order.

The practical implementation of a boost-converter-based G-semigyrator with controlled input current is shown in Fig. 10 for the set of parameters L1 = 75 µH, C1= 12µF, C2 = 20 µF and V2 = 24 V.

Fig. 10. Practical implementation of a boost-converter-based G-semigyrator operating at variable switching frequency with MPPT function

Note that variable vC depicted in Fig. 4 becomes the gyrator conductance of the power gyrator (Fig. 10). The variation of is with constant time-derivative is achieved by imposing such behavior to the gyrator conductance G.

Next, It will be shown the experimental behavior of Is, Vs, Ps of the PV generator and also de conductance g of the power semigyrator with the extremum-seeking control algorithm under different operating conditions. Fig. 11.a shows the PV system response after the connection of an additional panel in parallel with the PV generator. As it can be expected, the current increases while the voltage remains practically unchanged except in the transient-state connection. Since the voltage operating point has not changed, the maximum power point is almost instantaneously reached. However, when a different situation is observed in Fig. 11.b in which the panel previously added is removed. Now, the imposed input current is too large and the operating point of the PV generator remains during 20 ms in the short-circuit point delivering zero output power. The PV generator starts to deliver power when the conductance of the gyrator (g) diminishes until a value that implies a current i1 inside of v-i characteristic.


a)

b)

Fig. 11. Response to a parallel connection and removal of an additional panel (Time scale: 10 ms/div).

IV. EFFICIENCY EVALUATION The overall system efficiency of PV conversion

structure (ηTOTAL) is given by [9]

CONVMPPTPVTOTAL ηηηη = (11) where ηPV is the ratio of the maximum available

electrical power of the panel for the entering solar irradiance, ηMPPT is the ratio of the real available electrical power of the panel for its maximum available electrical power and ηCONV is the ratio of the power at the conditioner output for the power at the conditioner input.

Our automatic measuring system provides the values of ηMPPT and ηCONV along a complete day. Figs 12 and 13 shows this efficiencies values during an outdoor test of 6 hours. In this test we can compare the efficiencies performances obtained by means of a DC-power Transformer MPPT (Fig. 12) and by means of a DC-power gyrator (Fig. 13). The converter efficiency is better for the case of DC transformer; and this could be in part due to a higher consumption of the control circuitry and also to the variable switching frequency of the power gyrator. In fact, a variation in the switching frequency could imply a reduction of the converter efficiency. On the other hand the MPPT efficiency is bigger for the DC power gyrator for low levels of input power.

Fig. 12. Measured efficiencies of the boost converter-based voltage transformer with MPPT function

Fig. 13. Measured efficiencies of the boost converter-based G-semigyrator with MPPT function

Table I shows the energy balance and the averaged efficiencies during the 6 hours test. The total efficiency

Tη = MPPTη CONVη shows that we obtain slightly better efficiencies with the matching circuit performed by the DC transformer.

TABLE I. ENERGY BALANCE AND AVERAGED EFFICIENCIES

Available Energy

Absorbed Energy

Output Energy MPPTη CONVη Tη

Transformer 90.2 Wh 88.1 Wh 81.3 Wh 97.7 % 92.2 % 90.1 %

Gyrator 88 Wh 86.5 Wh 77.6 Wh 98.3 % 89.7 % 88.2 %

V. CONCLUSIONS In this work, we have compared the realization of

impedance matching circuits to track the maximum power point of a PV array by means of two concepts: the DC power transformer and the DC power gyrator.

A DC transformer-based boost converter has been implemented to match a lead-acid battery of 24 V with a PV array.

g

PS

VS

IS

g

PS

VS

IS


Also, it has been shown that power gyrators of type G with controlled input current can be used as impedance matching circuits to track the maximum power point of a PV array. The selected gyrator structure is the boost-converter-based G-semigyrator with controlled input current.

We have compared the dynamic and static performances of both possibilities by means of experimental verification. An outdoor test has been made to compare the averaged efficiencies in real conditions. The dc transformer-based boost converter has only an averaged efficiency 3 % bigger that the DC gyrator-based boost converter operating in sliding mode. It has to be pointed out that the transformer structure has a better dynamic performance when larges changes in the irradiation appear. This is due to the fact that when “the load is a battery” the input current varies almost instantaneously for the transformer case, while it takes some additional time in the case of the gyrator because the changes in the input current follows the change of the output voltage.

Similar studies are in progress for other converter structures like buck converter and Cuk converter.

VI. REFERENCES [1] S. Singer and A. Braunstein, “A General Model of Maximum

Power Point Trackers” Proceedings of MELECON’85. pp 147-151

[2] S. Singer and A. Braunstein, “Maximum power transfer from a nonlinear energy source to an arbitrary load” IEEE Proceedings, Pt G, 1987 pp 1-7

[3] A. Cid-Pastor, C. Alonso, B. Estibals, D. Lagrange and L. Martinez-Salamero, “Automatic measurement system for testing photovoltaic conversión chains”, 30th Annual Conference of IEEE Industrial Electronics Society, Proceedings of IECON 2004, Pusan, Korea, November 2004.

[4] J. Calvente, “Control en modo deslizante aplicado a sistemas de acondicionamiento de potencia de satellites”. Tesis doctoral, UPC, 2001. http://www.tdx.cesca.es (in spanish)

[5] R. Leyva, C. Alonso, I. Queinnec, A. Cid-Pastor, D. Lagrange and L. Martínez-Salamero, “MPPT of photovoltaic systems using extremum seeking control” IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 1, Jan. 2006, pp 249-258

[6] A. Cid-Pastor, L. Martínez-Salamero, C. Alonso, G. Schweitz, J. Calvente and S. Singer, “Classification and synthesis of power gyrators” IEE Proceedings Electric Power Applications (Accepted for publication)

[7] A. Cid-Pastor, “Energy Processing by means of Power Gyrators” Ph.D Dissertation. Technical University of Catalonia (UPC), Barcelona July 2005 (available at http://www.tdx.cesca.es)

[8] A. Cid-Pastor, L. Martínez-Salamero, C. Alonso, B. Estibals, J. Alzieu, G. Schweitz, and D. Shmilovitz , “ Analysis and design of power gyrators in sliding-mode operation” IEE Proceedings Electric Power Applications, Vol. 152, No. 4, July 2005.

[9] M. Jantsch, M. Real et al., “Measurement of PV maximum power point tracking performance”, 14th European Photovoltaic Solar Energy Conf., Barcelona 30 June- 4 July, 1997.


Hybrid and Solar Vehicles

Documents

Transcript of Hybrid and Solar Vehicles