Converting conventional cars in mild hybrid solar vehicles

Converting Conventional Cars in Mild Hybrid Solar Vehicles G.Rizzo*, M.Sorrentino*, C.Speltino**, I.Arsie*, G.Fiengo**, F.Vasca**

*Dept. of Mechanical Engineering, University of Salerno, 84084 Fisciano (SA), Italy

(Tel:+39 089 964069, e-mail: iarsie grizzo msorrentino @unisa.it)

**Dept. of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy

(Tel: +39 0824 305508, e-mail: gifiengo vasca cspeltin @unisannio.it)

Abstract: Hybrid electric vehicles (HEV) represent a good and feasible solution to reduce fuel

consumption and related emissions. Furthermore, there is an increasing interest toward the integration of

HEV’s with photovoltaic (PV) panels, in order to enhance their benefits. But, despite the recent

commercial success of HEVs, their market share is still insufficient to produce a significant impact on

global energy consumption. Moreover, due to the present crisis, a substantial substitution of conventional

vehicles with HEVs is unlikely, at least in short time. Consequently, a realistic strategy could consist in

developing solutions able to achieve part of the benefits of hybrid and solar vehicles on conventional

vehicles. A method for the mild solar hybridization of conventional vehicles is presented in this paper,

based on use of in-wheel motors on rear wheels, of a PV panels, of an additional battery and of control

system not interfering with the original ECU. A preliminary assessment of the benefits obtainable in

terms of fuel consumption as a function of the degree of mild hybridization is performed by simulation

analysis.

Keywords: Solar Energy, Solar Cell, Electric Vehicles, Hybrid Vehicles, Automotive Control

1. INTRODUCTION

Transportation has a marked impact on acoustic and

atmospheric pollution in urban areas, on fossil fuels depletion

and on greenhouse effect. Hybrid Electric Vehicles have

emerged as one of the most effective and feasible alternatives

to engine-driven vehicles, allowing significant reductions in

fuel consumption and emissions (Sciarretta and Guzzella,

2007).

Further substantial benefits can be achieved by Hybrid Solar

Vehicles, obtained by the integration of HEV’s with

photovoltaic panels, particularly for urban driving (Letendre

et al., 2003; Rizzo, 2010; Arsie et al., 2010; Rizzo et al.,

2010). The adoption of tracking solar roofs to maximize solar

contribution during parking phases is also under study

(Coraggio et al., 2010, I).

Fig. 1. Solar PV production and cost

In parallel, due to the pressing need for a renewable and

carbon-free energy (REN21, 2009), the diffusion of the

Photovoltaic has been growing exponentially in recent years

while their price is significantly decreasing (Fig. 1), and their

efficiency improving. The industrial interest toward

application of photovoltaic to cars is evidenced by the recent

launch of a new model of a HEV equipped with PV panels by

a major automotive company.

But, despite the recent commercial success of HEVs, their

market share is still insufficient to produce a significant

impact on energy consumption on a global basis. Moreover,

considering the current economic crisis, it is unlikely that, in

next few years, PV assisted EV’s and HEV’s will substitute

for a substantial number of conventional vehicles, since

relevant investments on production plants would be needed.

This fact could of course impair the global impact of these

innovations on fuel consumption and CO2 emissions, at least

in a short term scenario. Therefore, one may wonder if there

is any possibility to upgrade conventional vehicles to PV

assisted hybrid.

A such proposal has been recently formulated and patented at

the University of Salerno (www.hysolarkit.com). A research

project aiming at the development of a kit for mild-solar-

hybridization of conventional cars, proposed by University of

Salerno and University of Sannio, has been recently financed

by the Italian ministry of university and research.

In the following, the structure of the mild-hybrid vehicle are

presented and the main issues related to vehicle control

discussed. Then, the details of the models used for simulation

analysis are presented. Finally, the results of some

preliminary assessment of the benefits achievable with this

proposal are presented and discussed.

2. VEHICLE STRUCTURE

The vehicle structure is based on the conversion of a

conventional vehicle, where the front wheels are propelled by

an Internal Combustion Engine (ICE) controlled by an

Engine Control Unit (ECU). The ECU, as a rule, is equipped

by an On Board Diagnostics (OBD) gate that enables the

access to significant engine/vehicle operation data, such as

pedal position, vehicle and engine speed, manifold pressure,

thermal state, and environmental conditions. The mild

parallel hybrid structure is realized by substituting/integrating

Preprints of the 18th IFAC World CongressMilano (Italy) August 28 - September 2, 2011

Copyright by theInternational Federation of Automatic Control (IFAC)

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the rear wheels with in-wheel motors and by including an

additional battery pack and photovoltaic panels on the roof.

The upgraded vehicle can operate in i) pure electric mode -

when the ICE is switched off or disconnected by the front

wheels - or in hybrid mode - when the ICE drives the front

wheels while the rear wheels operate in traction mode or in

generation mode, corresponding to positive or negative

torque, respectively. The battery can be recharged by both in-

wheels motors, when operating in generation mode, and

photovoltaic panels. Optionally, the battery could be

recharged also by the grid, in Plug-In mode. A Vehicle

Management Unit (VMU) receives the data from the OBD

gate and the battery (for SOC estimation) to drive the in-

wheel motors, by properly acting on the electric node EN. A

display on the dashboard may advice the driver about the

actual system operation mode. Fig. 2 depicts a scheme of the

upgraded vehicle structure.

Fig. 2. Scheme of a system to upgrade a conventional car to Mild Hybrid

Solar Vehicle.

2.1. In-Wheel Motors.

One of the key aspects of this proposal concerns the

possibility of replacing the rear wheels with in-wheel motors.

This topic is very actual and of increasing industrial interest

(Fig. 3), being strongly related to the diffusion of electric

vehicles. In this sense, it is considered as a ‘disruptive

technology’ (Murata, 2010). Their use would also allow to

integrate advanced techniques for vehicle control (Braghin

and Sabbioni, 2010; Xiong at al., 2010) and to expand the

applicable range of vehicle control (Murata, 2010). Anyway,

the installation of a motor inside the wheel is made difficult

by the standpoint of space constraints. Moreover,

deterioration of ride comfort due to increase in unsprung

mass occurs. The complexity of these problems tends to

increase with motor power. Therefore, a parametric analysis

of the influence of in-wheel motor power on the expected

benefits of the hybridization strategy will be performed in the

following.

Fig. 3. Activities of OEM’s in the in-wheel motor technology (Gombert, 2007).

2.2. Control Strategies Issues

The powerflow control in the HEV is particularly challenging

because of the hybrid structure of the driveline and the

conflicting performance objectives: driver power demand,

fuel economy, SOC regulation, drivability. A flexible, model-

based, decoupling control strategy for power split in mild

hybridized vehicles would be suitable. In such case, the

control strategy should be composed of three main tasks: (1)

steady-state power management based upon consumption

minimization; (2) dynamic regulation of battery SOC, also

considering the energy contribution from PV panels during

driving and parking; (3) dynamic power split for energy

efficiency and drivability. Decoupling should be obtained in

the sense that control of battery SOC and drivability neither

does not affect the power demand nor does not influence each

other in the nominal operating conditions.

Regard to the operating modes achievable for the hybridized

vehicle, it should be noticed that in some vehicles pure

electric mode operation, with engine switched off, could not

be compatible with normal operation of steering and braking

systems. On the contrary, hybrid mode operation (with both

engine and electric motors working) should be always

possible, compatibly with driving behaviour, driveability and

safety issues. The following analysis is therefore referred to

the operation in hybrid mode only.

In that case, the energy management and control in the

proposed hybridization system would differ from classical

HEV by two main reasons.

The former is related to the addition of solar panels. In fact,

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. On the other hand, in case

of solar hybrid vehicle, as in Plug-In vehicles too, the battery

can be 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 (Arsie et al., 2006). Moreover, advanced

strategies could be necessary for the optimal management of

vehicle and battery, even adopting on-board weather forecast

to estimate the amount of solar recharge in next parking

phase (Coraggio et al., 2010, II).

The second reason is due to the fact that it would be

advisable to develop a kit which does not require any

modifications to the original ECU: in fact, the VMU would

drive the electric motors deriving its data from OBD port

(Fig. 2), and not interfering with the operation of the original

ECU. Of course, the need to design a control system

coexisting with the original ECU poses some specific

constraints in terms of driving requirements and driveability,

which result in different control strategies vs. those

implementable on a full hybrid vehicle. For instance, when

the driver steps on the accelerator in the hybridized vehicle,

so demanding higher vehicle power, an increase in the engine

power will necessarily result; on the contrary, in a ‘native’

HEV, where the splitting between thermal and electric engine

is managed by the control system, the increase in vehicle

power could be achieved even by reducing the engine power

and, in parallel, by increasing the electric motor power.

Similarly, in the hybridized vehicle a reduction in engine


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power will be always achieved when the driver releases gas

pedal.

Therefore, the achievement of a given level of power splitting

between thermal and electric motor can be obtained by

inducing the driver to modulate the pedal position until the

desired vehicle power would be reached. In other words, the

driver will act as the vehicle is running downhill. On the

contrary, the hybridized vehicle can operate in recharging

mode when the in-wheel motors absorb part of the power

generated by the engine: in this case the driver will act on the

pedal as the vehicle would run uphill. A detailed study of

driver behaviour is therefore needed to develop

implementable strategies for this kind of vehicle.

2.3. Vehicle Model

Vehicle simulation, whose results are presented in the next

Section, was performed by means of a longitudinal vehicle

model developed under the following hypotheses: i) the drag

force is considered acting on vehicle centre of gravity; ii)

vehicle inertia accounts for both vehicle mass (MHSV) and

rotational inertia of ICE, EM/EG and wheels, though the term

Meff; iii) the effects of elasticity in the mechanical

transmission are neglected. The resulting longitudinal model

relates requested power at wheels Pw to the road load, as

follows:

vdt

dvM

vACCvgMP

eff

xrHSVw

35.0sincos (1)

where and v are the road grade and vehicle speed,

respectively. In case of non negative Pw values, the

mechanical power is supplied by the ICE (PICE) and/or the in-

wheel motors (PEM), depending on the control variable PS

(Power Split) defined as:

0 w

w

EM PifP

PPS (2)

The control variable PS may range between negative values,

in case ICE provides an extra power to be recovered into the

battery, to unit (i.e. pure electric vehicle). In all cases the

requested power is given as the algebraic sum of ICE and in-

wheel motors power:

Pw=PICE+PEM (3)

In case of vehicle braking or deceleration (Pw<0), the

regenerative braking mode is active and the power is

recovered into to the battery, according to battery power

limitations and to in-wheel motors performance and

efficiency. These latter are computed from static maps

provided by the manufacturer as function of torque and

speed. Depending on in-wheel motors operation mode (motor

or generator) the electric power (PB) requested or supplied to

the battery is given by the following relationships:

0 w

tr

EMB Pif

PP

(4)

0 wEMwB PifPP (5)

2.4. Battery Model

A lithium-ion battery is composed of three principal parts:

two porous electrodes, namely cathode (positive) and anode

(negative) and an electrolyte solution that acts as separator

between the electrodes as shown in Fig. 4. During a discharge

process, the lithium active particles flow through the

electrolyte solution until they arrive at the positive electrode

The electrons produced in the negative electrode reaction

cannot flow inside the electrolyte, that acts as insulator, and

flow through an external circuit, producing the current. The

inverse reactions occur during the battery charge.

Due to their porous nature, the electrodes are represented as

small spheres of active material along the electrode, with the

chemical reactions occurring at the sphere surface Wang et

al. (1998a). The resulting Partial Differential Equations

(PDE) describe the battery system with four quantities, i.e.

solid and electrolyte concentrations (cs, ce) and solid and

electrolyte potentials (s, e) as function of the reaction

current density jLi

describing the current distribution along

the cell lenght x Gu and Wang (2000); Smith and Wang

(2006). The input variable of the model is the current I while

the output is the voltage at the metal collectors V.

Liex

effDex

eff jckkx

ln

(6)

Lisx

eff jx

(7)

Li

exeffDx

ee jF

tcD

t

c 01

(8)

ssrs cDt

c

(9)

where the current density is defined as:

(10)

The cell potential is computed as:

IA

RxLxV

fss )()( 0

(11)

where Rf is the film resistance on the electrodes surface and

A is the collectors surface. More details on the model and its

parameters can be found in Smith and Wang (2006),

Domenico et al. (2008a), and Wang et al. (1998b).

Fig. 4. Schematic macroscopic (x-direction) cell model with coupled

microscopic (r-direction) solid diffusion model. The electrodes are filled with the electrolyte solution.


9717

Reduced order model

The model obtained with the PDEs equations is useless for

real time applications or to derive a control systems because

of its extreme complexity. A more useful reduced order

model, derived in Di Domenico et al. (2008b), can be

achieved by neglecting the solid concentration distribution

along the electrode and considering the material diffusion

inside a representative solid material particle for each

electrode. In accordance with considering a mean solid

concentration, the spatial dependence of the Butler-Volmer

current can be ignored and a constant value jLi

is considered

which satisfies the spatial integral (for the anode or the

cathode)

(12)

where δn is the anode thickness. This averaging procedure is

equivalent to considering a representative solid material

particle somewhere along the anode and the cathode Di

Domenico et al. (2008b). The partial differential equation

(12), describes the solid phase concentration along the radius

of active particle, but the macroscopic model requires only

the concentration at the electrolyte interface. By using the

finite difference method for the spatial variable r, it is

possible to express the spherical PDE into a set of ordinary

differential equations (ODE), dividing the sphere radius in

Mr - 1 slices, each of size r = Rs/(Mr-1) and rewriting

boundary conditions Schiesser (1991). The new system

presents Mr-1 states cs = (cs1, cs2, …csMr-1 )T, representing

radially distributed concentrations at finite element node

points 1,…, Mr-1. Two sets of ODEs, one for the anode and

one for the cathode are then obtained in the classical form

(13)

where A, B and D are function of cell physical and chemical

properties. The positive and negative electrode dynamical

systems differ at the constant values and at the input sign. It

is convenient to define the normalized concentration, also

known as stoichiometry, x = cse,x/cse,max,x, with x = p, n for

the positive and negative electrode. The battery voltage (11),

using (13) and using the average values at the anode and the

cathode, can be rewritten as a collection of submodels

(14)

The results as well as more details can be found in Di

Domenico et al. (2008a) and Di Domenico et al. (2008b) and

are not reported here for brevity.

Finally, the battery voltage (14) can be rewritten as a function

of current demand and normalized solid concentration

(15)

where Kr is a term that takes into account both internal and

collector film resistances.

3. SCENARIO ANALYSIS

In order to assess the benefits associated to mild

hybridization of conventional cars, a simulation based

scenario analysis was carried out. Table 1 describes the basic

features and hypotheses of three selected scenarios. Case 1 is

nothing but the reference one, in that it provides the

conventional vehicle fuel economy. In Case 2 the benefits

achievable by only enabling regenerative braking trough in-

wheel motors are estimated. Finally in Case 3 a PV roof is

added to the mild hybrid powertrain, thus enabling the

achievement of further benefits in terms of fuel consumption.

The main difference between Case 2 and Case 3 energy

management policy lays in the final state of charge to be

reached at the end of the driving cycle, which in the current

scenario analysis is the standard NEDC driving path. Charge

sustaining strategy is adopted in Case 2, whereas in Case 3

the final SOC must differ from the initial one by the amount

of energy supplied by the PV roof during the parking phase

(Arsie et al. 2007). Particularly, it was assumed that driving

time is 1.5 hour/day, while parking phase lasts for 8.5 hours

on average during daytime (recharging with solar power),

while for the remaining parking time it is assumed that the

vehicle is not recharged. Considering the average daily solar

power that impacts on Southern Italy, the 300 Watts PV roof

assumed in Case 3 provides up to 1 kWh a day. It is worth

mentioning here that PV roof weight was assumed

approximately equal to 50 kg.

In both Cases 2 and 3 the hybrid components are managed as

follows:

0,,min max, tPtPtPtP wwIWMcIWMwb (16)

min

minmax,

&0,0

&0,,min

SOCtSOCtPtP

SOCtSOCtPtPt

tPtPStP

wb

wwIWM

cIWM

w

b

(17)

where P is power [kW], η [/] is efficiency and the footers w,

IWM, b and c stand for, respectively: wheel, in-wheel motor,

battery and converter. In this preliminary numerical activity,

the value of the power splitting PS was constantly set to 0.5.

It is also worth mentioning here that when Pw(t)>=0 the

remaining power demand is always met by the ICE. The

parameter SOCmin in (17) has been set as function of the

following variables: in-wheel motors power, size of battery

pack and presence of the PV roof (i.e. different final SOC,

i.e. SOCf, to be reached at the end of the driving path).

Regarding battery size, in the scenario analysis it was

imposed that the number of series connected lithium cells

always equals 50 (i.e. battery voltage is 185 Volts), whereas

the number of parallel connected cells varies in order to

match the nominal in-wheel power.

Table 1. Description of analyzed mild hybridization scenarios.

Case Regenerative braking PV roof

1 NO NO

2 YES NO

3 YES YES

The impact of additional weight due to Mild Hybridization is

also considered, in order to estimate their effects on fuel

consumption. In next developments, a study of additional

costs will be also included, in order to perform an optimal

design of the system considering both technical and

economic aspects and to assess the practical feasibility of the

proposed hybridization technique.

4. RESULTS

In this section the main outcomes of the scenario analyses

described in the previous section are presented and discussed.

Particularly, Case 2 and 3 were simulated varying the


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nominal in-wheel power (Pn,IWM), in the range [1, 12] kW (the

nominal power of each wheel is therefore the half of such

value). Table 2 lists the main specifications of the simulated

vehicle.

Table 2. Conventional car specifications.

Nominal ICE power [kW] 46

Fuel gasoline

Coefficient of drag (Cd) 0.35

Frontal area [m2] 2.24

Rolling radius [m] 0.294

Rolling resistance coefficient [/] 0.01

Base vehicle mass [kg] 1000

Next figures illustrate the impact of in-wheel motor and

battery size on fuel economy with and without PV roof. Fig.

5 shows that energy benefits due to mild hybridization

always increase in Case 2, reaching an asymptotic behavior

beyond about Pn,IWM = 7 kW. On the other hand Case 3

exhibits a maximum fuel economy between 6 and 8 kW,

beyond which fuel savings with respect to the reference case

decrease, mainly due to the weight increase, as shown in Fig.

6, and to variations in average in-wheel motor efficiency.

Furthermore, the comparison of Case 2 and Case 3 highlights

the importance of coupling in-wheel motors to the PV roof to

significantly enhance the energy saving effect achievable via

mild hybridization. The aforementioned optimal Case 3 value

corresponds with fuel savings as high as 15 % as compared to

the conventional car.

Fig. 6 shows the variation of weight increase due to mild

hybridization in Case 3. The curve is not perfectly linear

because battery size was modified varying in discrete way the

number of parallel connected cells. Therefore, the same

battery size was adopted for near Pn,IWM values (e.g. Pn,IWM =

5 and Pn,IWM = 6 in Fig. 6). The simultaneous analysis of

figures 5 and 6 suggests once again the opportunity of

selecting a reasonable maximum power for in-wheel motors.

Higher power not only may not further increase energy

savings, but also leads to unnecessary weight and inertia

increase of the hybridized vehicle.

Finally, Fig. 7 shows the variation of the target SOCf to be

reached at the end of the driving cycle in Case 3. As

expected, higher Pn,IWM results in larger battery size and,

therefore, in higher SOCf and smaller SOC variation with

respect to the initial value (i.e. SOC0=0.7).

Finally, it should be noticed that the presented analysis has

not yet exploited all the potentialities of this system. For

instance, in case that the vehicle requires low torque,

corresponding to low engine efficiency (i.e. point A in Fig.

8), it could be convenient to operate the engine at higher

torque, for instance corresponding to the maximum efficiency

at the given engine speed (i.e. point B), and recovering the

excess power by operating the electric motors in generating

mode, as it happens in full hybrid vehicles. Of course, the

convenience should be assessed considering the conversion

efficiencies involved, also taking into account the maximum

torque that in-wheel motor can deliver at the given speed.

Preliminary tests have confirmed that fuel consumption can

be improved with respect to the presented results by adopting

such strategy. Anyway, some issues related to the

implementability of this solution within the hybridization

strategy should be investigated.

More in general, this preliminary analysis should be

integrated by a more systematic study on the optimal

management strategy for the hybridization system. A not

causal reference solution, to be adopted as a benchmark for

on-board implementable strategies, is in course of study by

Dynamic Programming (DP) approach (Sundström and

Guzzella, 2009).

5. CONCLUSIONS

The availability of a system to upgrade conventional vehicles

to mild-solar-hybrid could have a relevant impact on fuel

consumption and CO2 emissions due to transportation, since

it may potentially be applied to most of the today fleet, and

without requiring expensive reconversion of production lines

for cars. A discussion on the main features of a such system,

on the open problems and on main control issues has been

presented. A preliminary study by simulation analysis has

confirmed that the proposed mild-solar-hybrid conversion

can give significant benefits in terms of fuel consumption,

even adopting in-wheel motors with limited power.

Further work is in progress to: i) develop optimal control

strategies that are suitable for online implementation (i.e. real

world application), ii) to address both safety and functionality

issues associated to car retrofitting, mainly due to the need of

addressing the interaction among driver action on

acceleration and brake pedal and the additional VMU; iii)

finally, develop a prototype of the whole mild-solar-

hybridization system.

Fig. 5. Variation of fuel economy as function of degree of mild

hybridization.

Fig. 6. Weight increase due mild solar hybridization.

Fig. 7. Final SOC to be reached in Case 3 as function of degree of mild

hybridization.

1 2 3 4 5 6 7 8 9 10 11 1218

19

20

21

22

23

24

25

Pn,IWM

[kW]

[km

/l]

Fuel economy [km/l] vs. nominal in-wheel power

Case 1

Case 2

Case 3

1 2 3 4 5 6 7 8 9 10 11 120

25

50

75

100

125

150

Pn,IWM

[kW]

[kg

]

Weight increase [kg] due to mild hybridization - Case 3

solar kit

in-wheel motors

PV

battery

1 2 3 4 5 6 7 8 9 10 11 120.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

Pn,IWM

[kW]

Final SOC [/] vs. nominal in-wheel power - Case 3

SOC0

SOCf


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Fig. 8. Engine efficiency map and possible operating points in recharging

mode.

6. REFERENCES Arsie, I., Rizzo, G., Sorrentino, M. (2007). Optimal Design and Dynamic Simulation of

a Hybrid Solar Vehicle. SAE 2006 Transactions – Journal of Engines, Vol. 115-3,

pp. 805-811.

Arsie, I., Rizzo, G., Sorrentino, M. (2010). Effects of engine thermal transients on the

energy management of series hybrid solar vehicles. Control Engineering Practice,

doi: 10.1016/j.conengprac.2010.01.015.

Braghin F., Sabbioni E. (2010), Development of a control strategy for improving

vehicle safety in a hybrid vehicle with four independently driven in-wheel

motors, 10th International Symposium on Advanced Vehicle Control AVEC10,

August 22-26, 2010, Loughborough, UK.

Coraggio G., Pisanti C., Rizzo G., Senatore A. (2010, I), Model Based Control of a

Moving Solar Roof for a Solar Vehicle, In: 10th International Symposium on

Advanced Vehicle Control AVEC10, August 22-26, 2010, Loughborough, UK.

Coraggio G., Pisanti C., Rizzo G., Sorrentino M. (2010, II), Assessment of benefits

obtainable in a Hybrid Solar Vehicle using look-ahead capabilities for incoming

solar energy, In: 10th International Symposium on Advanced Vehicle Control

AVEC10, August 22-26 2010, Loughborough, UK.

Di Domenico, D., Fiengo, G., and Stefanopoulou, A. (2008a). Lithium-ion battery state

of charge estimation with a Kalman filter based on a electrochemical model.

Proceedings of 2008 IEEE Conference on Control Applications.

Di Domenico, D., Stefanopoulou, A., and Fiengo, G. (2008b). Reduced order lithium-

ion battery electrochemical model and extended Kalman filter state of charge

estimation. ASME Journal of Dynamic Systems, Measurement and Control -

Special Issue on Physical System Modeling.

Gombert B., (2007), http://www.munichnetwork.com/

fileadmin/user_upload/konferenzen/mobilitaetsforum-

2/071128MUN_Gombert_Bernd.pdf

Gu, W. and Wang, C. (2000). Thermal and electrochemical coupled modeling of a

lithium-ion cell. Proceedings of the ECS, 99, 748-762.

Letendre, S., Perez, R., Herig, C. (2003). Vehicle Integrated PV: A Clean and Secure

Fuel for Hybrid Electric Vehicles. Proc. of the American Solar Energy Society

Solar 2003 Conference, June 21-23, 2003, Austin,TX.

Lu Xiong, Zhuoping Yu and Yufeng Meng, (2010), Vehicle Dynamic Control for a 4

In-Wheel-Motored EV based on Identification of Tire Cornering Stiffness, 10th

International Symposium on Advanced Vehicle Control AVEC10, August 22-26,

2010, Loughborough, UK.

Murata S., (2010), Innovation by In-Wheel-Motor Drive Unit, Keynote lecture, 10th

International Symposium on Advanced Vehicle Control AVEC10, August 22-26,

2010, Loughborough, UK.

REN21, Renewables - Global Status Report - 2009 Update,

http://www.ren21.net/pdf/RE_GSR_2009_update.pdf

Rizzo, G. (2010). Automotive Applications of Solar Energy. 6th IFAC Symposium

"Advances in Automotive Control", AAC10, July 11-14, 2010, Munich

(Germany).

Rizzo, G., Sorrentino, M., Arsie, I. (2010). Rule-Based Optimization of Intermittent

ICE Scheduling on a Hybrid Solar Vehicle. SAE International Journal of Engines,

Vol. 2, No. 2, pp. 521-529.

Schiesser, W. (1991). The Numerical Method of Lines: Integration of Partial

Differential Equations. Elsevier Science & Technology.

Sciarretta, A., Guzzella, L. (2007). Control of Hybrid Electric Vehicles. IEEE Control

Systems Magazine, Vol. 27, No. 2, pp. 60-70.

Smith, K. and Wang, C. (2006). Solid-state diffusion limitations on pulse operation of a

lithium-ion cell for hybrid electric vehicles. Journal of Power Sources, 161, 628-

639.

Sundström O. and Guzzella L. (2009), A Generic Dynamic Programming Matlab

Function, 18th IEEE International Conference on Control Applications, Part of

2009 IEEE Multi-conference on Systems and Control, Saint Petersburg, Russia,

July 8-10, 2009

Wang, C., Gu, W., and Liaw, B. (1998a). Micromacroscopic coupled modeling of

batteries and fuel cells. part i: Model development. J. Electrochem. Soc., 145,

3407-3417.

Wang, C., Gu, W., and Liaw, B. (1998b). Micromacroscopic coupled modeling of

batteries and fuel cells. part ii: application to ni-cd and ni-mh cells. J.

Electrochem. Soc., 145, 3418-3427.

7. NOMENCLATURE Symbol Name Unit

Road Grade Deg

e electrolyte potential V

s solid potential V

ce electrolyte concentration mol cm-3

Cr Rolling resistance coefficient

cs solid concentration mol cm-3

cse solid concentration at electrolyte

interface

mol cm-3

Cx Drag coefficient /

I battery current A

ie electrolyte current density A cm-2

is solid current density A cm-2

jLi Butler-Volmer current density A cm-3

MHSV Vehicle Mass kg

P Power kW

PS Power Split index /

SOC State of Charge /

Un anode open circuit voltage V

Up cathode open circuit voltage V

v Vehicle Speed m/s

θn normalized solid concentration at anode -

θp normalized solid concentration at

cathode

-

Pedices

B Battery

EM Electric Motor

ICE Internal Combustion Engine

IWM In-Wheel Motor

W Wheels

Acronyms

HEV Hybrid Electric Vehicle

HSV Hybrid Solar Vehicle

PV Photovoltaic

8. ACKNOWLEDGEMENTS

This research has been financed by the Italian Ministry of

University and Research, within the PRIN Projects

(http://www.dimec.unisa.it/PRIN/PRIN_2008.htm).

0.10.150.15

0.15

0.2

0.20.2

0.2

0.25

0.250.25

0.2

5

0.25

0.3

0.3

0.3

0.3

0.3

1

0.31

0.31

0.31

0.32

0.32

0.32

0.32

0.33

0.3

3

A

B

Engine efficiency map

Engine speed [rpm]

Engin

e t

orq

ue [

Nm

]

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000

10

20

30

40

50

60

70

80

90


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Converting conventional cars in mild hybrid solar vehicles

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