Hybrid Solar Vehicles: Perspectives, Problems, Management Strategies

Hybrid Solar Vehicles: Perspectives, Problems, Management StrategiesIN

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Hybrid Solar Vehicles: Perspectives, Problems, Management Strategies

HYATT REGENCYNOVEMBER 13-14, 2008

ISTANBUL

I.Arsie, G.Rizzo, M.SorrentinoDIMEC, University of Salerno, Italy


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Outline

IntroductionHSV: models and resultsOptimization of Management StrategiesThe PrototypeConclusions


The background

Serious alarms about global

warming and climate

changes related to CO2

concentration in the

atmosphere

Growing demand for mobility. The Chindia factor, 1/3 of world population. 400% and 205% increase in cars for China and India from 1990 - 2000

CO2 emission for transport is increased in last 30 years both in relative and absolute values. (UK data. Similar trends hold in Western

countries).


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From conferences to cartoons


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Possible Solutions?

Kyoto Protocol: A possible solution to fossil fuel depletion and global warming is an increased recourse to Renewable Energy (RE).Possible application to cars:

Fuels/Energy from RE (Bio-Fuels, H2)

Solar Cars


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UL Solar Energy

1.5 106 km

A very small part of the energy radiated by sun strikes the Earth (a part over two

billions).

Nuclear fusion into the sun produces an enormous amount of

energy, irradiated into the space.

Solar energy is partly reflected to the space (15%), partly used to evaporate water (30%)

and partly absorbed by plants, oceans and land, and for men use (55%).


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Solar Energy vs. Energy Consumption

The solar energy striking the US in one day is almost equivalent to the energy consumption for one

and a half year

= +


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UL PV Panels

Much of today's research in multi-junction cells focuses on gallium arsenide as one of the component cells. Such cells have reached efficiencies of around 40% under concentrated sunlight (Fresnel lens).

Today's most common PV devices use a single-junction with poli-crystalline silicon, with efficiency of about 12%

Use of mono-crystalline silicon results in higher efficiency (15% and more)

Multi-junction cell


PV efficiency trends

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Solar Panels Production and Prices

The production of photovoltaic panels has remarkably increased

since 90’s in terms of installed power.

Their cost, after a continuous decrease and an inversion of the trend occurred in 2004, appears now quite stable


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Outline



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Solar Cars

Various propotypes of solar cars have been developed, for

racing and demonstrative use


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UL Limits of Solar Cars

Solar Cars do not represent realistic alternative to “normal” cars, due to:

Limited power and performance.Limited range.Discontinuous energy source.High cost.


Hybrid Electric Vehicles

Honda Insight

Toyota Prius

Ford EscapeGM Precept

F.Porsche, 1900Buick Skylark, 1974

Peugeot 308 Hybrid-

Diesel

Mercedes S400 Hybrid-

Diesel


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HEV and PV: a possible marriage?


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About the dowry

Conventional/Hybrid car

PV panels

Energy 600 KWh 50 kg gasoline tank

<50 KWh/day6 m2 @ 8.5KWh/m2/day

Power 100 KW < 1 KW

Q: Is solar energy a rich dowry for a vehicle?

Solar Cars: lighter than CarsHEVs: heavier than Cars


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Energy Balance in a Solar Car

sun

dsunPVp

sun

dsunsunPVpdpsun h

heA

h

hheAEEE

Net solar energy available to propulsion [KWh/day]

esun=average insolation (KWh/m2day)APV=effective panel area = APV,H+0.5 APV,V

PV=panel efficiency (=0.13): reduction factor due to charge/discharge processes in battery (=0.9): insulation reduction during driving, due to shadow (=0.9)

Daily time fraction spent in parking mode

Daily time fraction spent in driving modeParking mode Driving mode


0 5 10 150

20

40

60

80

100

Car Average Power [KW]

Sol

ar E

nerg

y %

APVH[m2]=6 APV

V[m2]=0 Vol.[m3]=8.8997

h=1h=2h=3h=5h=10

Solar Fraction

Site: San Antonio, TexasYearly Averaged Data

Continuous use (h=10) with 100% recourse to the sun can be achieved only at very low

power (<1 KW).

Solar energy can represent a significant contribution for

intermittent use (h=1-2) and for limited average power.

For average power from 5 to 10 KW and driving hours from 1 to

2, solar contribution ranges from 18% to 60%.

6 m2@12% or 3 m2@24%

Driving

hours

per

day

Are these values of power and driving hours significant?


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Statistics on Car Drivers

about 71% of UK users reaches their office by car46% of them have trips shorter than 20 min mostly with only one person on board.

Source: Labour Force Survey, http://www.statistics.gov.uk/CCI/nscl.asp?ID=8027

Some recent studies of the UK government stated that


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Power Demand

0 200 400 600 800 1000 12000

50

100

150Speed [km/h]

0 200 400 600 800 1000 1200-40

-20

0

20

40

60

Time [s]

Power [KW]

Power demand can be determined integrating the

longitudinal vehicle model over a mission

cycle.

During urban drive, limited average

power can be required to drive a

small car.

Urban Extra-urban

Mass=1000 Kg - Length=3.75 m


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UL Effects of Position on Energy

0

500

1000

1500

2000

2500

3000

0 20 40 60 80

2 axis tracking1 axis trackingTilt=LatitudeHorizontalVertical (mean)

Latitude (deg)

Average Yearly Energy (KWh/year)

Almost a factor 2 between maximum and minimum latitudes.

For fixed panels, there is not a relevant loss by adopting horizontal position with respect to “optimal” tilt, particularly at low latitudes.

Negligible differences between 2-axis and 1-axis tracking systems.

Energy absorbed with vertical position is significantly lower, mainly at low latitudes.

46%

79%

Adoption of moving solar roof for parking phases can significantly increase solar energy, particolarly at the high latitudes


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UL Some HSV prototypes

Solar Toyota PriusBy Steve Lapp

Ultra-CommuterThe University of Queensland

Viking 23Western Washington University Tokyo University of Agriculture

and Technology


Solar Prius

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It is estimated that the PV Prius will consume somewhere between 17% and 29% less gasoline than the stock Prius (range per day: 5-8 miles)

Prius with an aftermarket 215 W monocristalline solar module with peak power tracking and a 95%

efficiency DC-DC Converter


Well to Wheel

H2


HSV vs HEV

HEV ≠ Conventional Car + Electric Motor

HSV ≠ HEV + PV


HSV vs HEV

Mission profile (HSV should be optimized for urban driving)Different SOC management strategies.Different structure (vehicle dimension, hybrid architecture)

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HSV vs HEV controlSOC

Time

driving pathIn most HEVs, a charge sustaining strategy is

adopted: the battery State Of Charge (SOC) is unchanged

within a driving path.

ΔSOC

SOC

Time

driving path parking

dayA suitable strategy for HSV instead can restore the initial

SOC within a whole day, considering battery charging

during parking time.

Charge depletion


Potential advantages of Series HSVNo mechanical link between generator and wheels:

Very effective vibration insulation can be achievedLess constraints for vehicle layoutPossible use of in-wheel motors with advanced traction control techniques

Engines optimized for steady operation can be used:

ICE designed and optimized for steady conditionsD.I. Stratified charge engine (4 or 2 strokes)Micro gas turbine

Series architecture acts as a bridge towards the introduction of fuel cell powertrains.More suitable for V2G applications


Vehicle to Grid (V2G)V2G concept: to connect parked electric driven vehicles (electric, hybrid, hybrid solar, fuel-cell) to the grid by a two-way computer controlled hook up.The power capacity of the automotive fleet is about 10 times greater than the electrical generating plants (in US) and is idle over the 95%.

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Advantages:Reduction of costs for peak power production.Toward the distributed generation, with reduction of Transmission and Distribution (T&D) costs.Facilitate integration of intermittent renewable resources.The value of the utility exceeds the costs for the two-way hook up and for the reduced vehicle battery life.


V2G: Additional advantages for HSV

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Possible use of moto-generator as cogenerator

for domestic use

Possibility to transfer excess solar power

to the grid


Engine control in a series HSV

ICE Efficiency

A BICEP

optP MAXP

ICE Power

In a series HSV, the Internal Combustion Engine could operate on the optimum

efficiency curve and whenever possible at its maximum efficiency

Part load operation can be avoided and substituted by intermittent operation at

maximum efficiency


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UL SHM – Operating Modes /1

ICE EG

PV Panels

EM

Parking

with sunlight

Battery

VMU


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ICE EG

PV Panels

EM

Hybrid

with sunlight

Battery

VMU


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ICE EG

PV Panels

EM

Electric Driving

with sunlight

Battery

VMU


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ICE EG

Battery

PV Panels

EM

Regenerative Braking

with sunlight

VMU


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ICE EG

Battery

PV Panels

EM

Recharge from grid

with sunlight

VMU


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ICE EG

Battery

PV Panels

EM

Power to grid (V2G)

with sunlight

VMU

Thermal load

heat

“We believe that the most plausible vehicle of the future is a plug-in hybrid...”

(Center for Energy and Climate Solutions, 2004)


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UL Flow Chart

OUTPUTCar Stability – Fuel Savings – Weight - Payback

Objective Function and Constraints

MODELSEnergy Flows for HSV/CC – Car sizing - Weight - Cost

DESIGN VARIABLESPV Panel Area and Position

– EG and EM Power – Car dimensions – Materials

DESIGN SPECIFICATIONPower demand – Insolation –

HSV Structure

EXHOGENOUS VARIABLESFuel Price – Panel

Efficiency – Unit weight and costs

CONTROL VARIABLESControl Strategy for

EG – MPPT for PV


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Payback Optimization

Objective Function: minimum Payback XPBXmin

Gi NiXG ,10

Design variables X:

1. Electric Generator Power PEG2. Electric Motor Power PEM3. Horizontal panel area APV,H4. Vertical panel area APV,V5. Car length l6. Car width w7. Car Height h8. Weight reduction factor of car chassis with respect to base value CWf

Inequality Constraints

Solved by Sequential Quadratic Programming (Matlab routine FMINCON)


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Power to Weight ratio equal to the conventional vehicle1

CV

HSV

PtW

PtW

maxmin

maxmin

maxmin

hhh

www

lll

Car dimensions within assigned limits, obtained by the database of commercial vehicles

maxmin

maxmin

w

h

w

h

w

h

w

l

w

l

w

lLength to width ratio and height to width ratio within assigned limits, obtained by the database of commercial vehicles

hwlAA

wlAA

VPVVPV

HPVHPV

,,

,

max,,

max,,

PV panels area compatible with car dimensions, according to the given geometrical model

EG Power within lower and upper boundsmax.min, EGEGEG PPP

7.0CWf Car weight reduction factor not lower than 0.7


#cf

€/kgcPV

€/m2 €/W P [/] APV,H [m

2] PEG [kW] PB [yrs]

1 1.77 800/6.15 0.13 0 35.5 6.1

2 1.77 800/6.15 0.13 3 35.5 9.9

3 1.77 200/2.15 0.13 4 37 5.6

4 3.54 200/2.15 0.16 5.6 38.4 2.4

A very good payback (2.4 years) is by doubling fuel cost, reducing by 4 panel cost,

and considering 16% panel efficiency

Optimal design results

Fuel Price ≈ 2.1 €/KGItaly, June, 2008

PV Retail Price:June 2008: 4.70 €/W


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UL MPPT Techniques

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Power vs. voltage characteristic of a PV field

Uniform working conditions

Mismatched PV field

Due to changing sun irradiance, PV source must be matched to the load to draw maximum power.Maximum Power Point Tracking (MPPT) techniques are adopted.The presence of local maxima occur during mismatched conditions, due to shading effects and temperature variations in different parts of the panel.The characteristic may change rapidly during driving conditions, required advanced MPPT control.


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Sources of mismatchingDifferent solar irradiation levels due to:

CloudsShadowsDifferent orientation of parts of the PV fieldDirtinessTolerances (due to manufacturing and/or ageing)

Different types of panels (different models, photo-glass, coloured) in the same string


MPPT management of PV arrayMPPT strategy are implemented to maximizing PV efficiency throughout the day.

P

Vi

MPPT

Vi

• Power given to the battery

• Max Allowable Power


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Outline



HSV Modeling

Longitudinal model of the HSV protoype:

vdt

dvMvACCvgMP effxrHSVw 35.0sincos Power at wheels

EM Power

0/ wPVDCACEGEMB PifPPPP

0

0

0

/

wEMtrwEM

wPVBDCACEGEMEM

wtr

wEM

PifPP

PifPPPP

PifP

P

Battery recharge power

= experimentally characterized


Experimental characterization: EG and EM

The electric generator was characterized connecting a pure resistive electrical load.A 4° order polynomial regression was obtained.

0 1 2 3 4 5 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Power [KVA]

EG

[/]

Experimental

Simulated

EM efficiency is modeled by a 3rd order polynomial regression identified vs. manufacturer technical data.

0 2 4 6 8 10 12 140.65

0.7

0.75

0.8

0.85

0.9

Power [KW]

EM

[/]

experimental

simulated


Experimental characterization: the Battery pack

Battery is modeled applying the Kirchoff law to an equivalent circuit.

The internal resistance was modeled as a nonlinear function of state of charge.

Model accuracy was checked against experiments.

Rin

__E0

Vr

Vbatt

E0= battery open circuit voltage

Idis= discharging current

Vr= internal voltage losses

Rin

__E0

Vr

Vbatt

Rin= battery internal resistance

Vbatt= effective voltage

Ichg= charging current

Idis Ichg

a) b)

0 20 40 60 80 1005.8

6

6.2

6.4

Current [A]

Battery voltage [V] in discharge operation mode (a)

ExperimentalBattery Model

0 20 40 60 80 1006.2

6.4

6.6

6.8

7

7.2

Current [A]

Battery voltage [V] in charge operation mode (b)

ExperimentalBattery Model 0 0.2 0.4 0.6 0.8 1

0.005

0.01

0.015

0.02

0.025

State of charge [/]

Battery internal resistance [Ohm]

DischargeCharge


Experimental characterization: the PV array

The PV array has been characterized by connecting the converter output to a resistive load.

0 5 10 15 20 25 30 35 40 450

10

20

30

40

50

60

PV Voltage [ V ]

PV

Pow

er [W

]

experimentalsimulated

Daily Average Energy [kWh/kWp/day]

0

1

2

3

4

5

6

PV = 10 %(390 W/m2 irradiation)

The average PV daily energy was derived from an experimental year-thorough distribution:

dayWhA

EE

daykWp

kWhE

PVdaysunPV

daysun

/45010

44.11.3

10

1.3

,

,


ICE thermal transients

K

t

ssinss eTTTtT

Engine temperature dynamics is estimated by a first order dynamic model

60027OFF

15082ON

K [s]Tss [°C]ICE operation

Steady state temperatures and time constants are assigned

for ICE on and ICE off events0 1000 2000 3000 4000 5000

20

30

40

50

60

70

80

90

Time [s]

T [°

C]

Engine temperature

N = 1N = 4


Thermal effects on power and SFC

ss

engss T

TfPtP

ss

eng

ss

T

Tf

PSFCtSFC

Specific Fuel Consumption and power are related to the ratio

between actual temperature and its steady state value, starting from experimental data for a SI

engine

3

21

ss

eng

T

T

ef


Modeling of HC emissionsDue to the ICE intermittent use, HC emissions occurring during warm-up have to be accounted for.

0 50 100 150 200 250 300 350 400 45020

30

40

50

60

70

80

Time [s]

Engine Temp. [°C]

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200

400

600

800

1000

Time [s]

HC [ppm]

Experimental warm-up HC dynamics

]20:..] s:

engTbatHC

0 5 10 15 200

200

400

600

800

Time [s]

HC [ppm]

Tin

= 26 °C

Tin

= 55 °C

K

t

ssinss eHCHCHCtHC

[0-20] s: HC formation mechanism modeled as a first order process

in

inin TTHC

1


Day through charge sustaining is achieved constraining SOC variations.

Energy management strategy

dtXm HSVfX ,min

minSOCSOC

maxSOCSOC

Minimum and maximum values considered for state of charge

Power

Time

Traction power ICE Power

In case of ICE intermittent use, energy management for HSV can be addressed via an optimization analysis.

The decision variables X include number of ICE starts, starting time, duration and ICE power level.

00 pfday SOCSOCSOCSOC


Simulation of HSV prototype: scenario analysis

0 2 4 6 8 10 120

5

10

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20

25

30

35

Time [min]

Vehicle speed [km/h]

The prototype was simulated on a driving cycle composed of 4 ECE-like modules.

ICE power [kW] 46

Fuel gasoline

PEG [kW] 43

PEM [kW] 90

Number of battery modules [/] 27

PV horizontal surface APV,H [m2] 1.44

Coefficient of drag (Cd) 0.4

Frontal area [m2] 2.6

Weight [kg] 1465

HSV Specification


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90

Time [s]

Engine temperature [°C]

N = 2N = 4N =8

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-20

0

20

40

60

Time [s]

HSV power [kW]

Traction powerEG

N2

EGN4

1 2 3 4 5 6 7 80

5

10

15

20

N. of starts

Fuel economy improvement [%]

Control optimization results (DBM) 1/3

• Initially fuel economy increases with engine starts due to the higher degrees of freedom.

• After 4 ICE-on, fuel economy tends to decrease due to the increasing impact of thermal transients.

engTN

PEG,N4 > PEG,N2


• HC emissions show an increasing trend with number of starts.

• A local minimum occurs at N =4.

• Such a behavior is due to the different temperature trajectories.

1 2 3 4 5 6 7 80

0.5

1

1.5

2

N. of starts

HC [grams]

0 1000 2000 3000 4000 500020

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40

50

60

70

80

90

Time [s]


N = 2N = 4N =8

0 1000 2000 3000 4000 500020

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40

50

60

70

80

90

Time [s]

Engine temperature [K]

N = 3N = 4

0 1000 2000 3000 4000 50000

10

20

30

40

Time [s]

HC emissions [g/h]

N = 3N = 4




0 1000 2000 3000 4000 5000-30-15

015304560

Time [s]

HSV power [kW] - N = 4 (a)

drivegen

0 1000 2000 3000 4000 50000.65

0.7

0.75

0.8

Time [s]

State of charge [/] N = 4 - (b)• SOC excursions

are satisfactorily bounded

• Final SOC leaves room for PV charging during parking phases

• On average EG operating conditions fall in a high efficiency region.


Energy management optimization by means of genetic algorithm (GA) search

Binary representation of the optimization problem

As both integer and real variables are involved, the GA search method was selected for such an analysis.HC emissions and fuel consumption for cranking energy have been also included in the objective function.

GA parameters

Population size 70

Number of generations 100

Crossover probability 0.8

Mutation probability 0.033

Decision variable

Definition range

PrecisionNumber of bits

NEG [1 8] 1 3

tEG (min) [0 78/ NEG] 0.073/ NEG 10

tEG (min) [0 78/ NEG] 0.073/ NEG 10

PEG (kW) [0 43] 0.040 10


Control optimization results (GA) 1/2

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0 10 20 30 40 50 60 70 80-40

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Time [min]

EG and battery power trajectories [kW]

EG

Battery

0 10 20 30 40 50 60 70 8020

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Time [min]


DBM

GA


Control optimization results (GA) 2/2

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0 10 20 30 40 50 60 70 80-50

-25

0

25

50

Time [min]

Power at wheels [kW]

0 10 20 30 40 50 60 70 800.4

0.6

0.8

1

Time [min]

SOC variation [/] To be recovered in the parking phase


Comparison between GA and DBM results

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Optimization outputs DBM GA 1

NEG 4 3

Fuel consumption [kg] and % saving (*)

2.41 (14.8%)

2.48 (12.4%)

HC emissions 1 (g) 1.13 0.85

Average engine temperature [°C] 65 68

Max SOC [/] 0.79 0.88

Min SOC [/] 0.65 0.58

HC emissions 2 (g/km) 0.025 0.018

A further optimization analysis was run considering an increase in PV horizontal area from 1.44 m2 to 3 m2. Such configuration upgrade results in a fuel consumption reduction

from 2.48 kg to 2.28 kg (19.4% saving).

(*) conventional vehicle fuel consumption = 2.83 Kg


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Outline



HSV PrototypeVehicle Piaggio Porter

Length 3.370 m

Width 1.395 m

Height 1.870 m

Drive ratio 1:4.875

Electric Motor BRUSA MV 200 – 84 V

Continuous Power 9 KW

Peak Power 15 KW

Batteries 16 6V Modules Pb-Gel

Mass 520 Kg

Capacity 180 Ah

Photovoltaic Panels Polycrystalline

Surface 1.44 m2

Weight 60 kg

Efficiency 0.13

Electric Generator Diesel Yanmar S 6000

Power COP/LTP 5.67/6.92 kVA

Specific fuel cons. 272 g/kWh

Weight 120 kg

Overall weight (with driver)

Weight 1950 kg

A prototype of hybrid solar vehicle with series structure has been developed at the University of Salerno, within the EU Leonardo Program “Energy Conversion

Systems and Their Environmental Impact” (www.dimec.unisa.it/leonardo)


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UL A research and educational project

http://www.dimec.unisa.it/leonardoSponsored by ACS, Salerno (I), Lombardini (I), Saggese (I).

Leonardo Program (I05/B/P/PP-154181)Energy Conversion Systems and Their Environmental Impact


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UL The web site

www.dimec.unisa.it/LeonardoA multi-lingual web site has been developed.

The site has more than 1000 visits per week

and is at the top positions on Google.


Participation to the FIA Alternative Energies Cup race ECO-TARGA FLORIO (Palermo, Italy)


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Outline



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ConclusionsHybrid Solar Vehicles can represent a valuable solution for energy saving and environmental issues, but accurate re-design and optimization of both vehicle and powertrain with respect to HEV are required.Economic feasibility could be achieved in a near future, with realistic assumptions for component costs, fuel price and PV panel efficiency.Significant fuel savings can be obtained by proper ICE management strategies. Thermal transient effects on fuel consumption and HC emissions must be considered in case of intermittent use.The use of optimization techniques (GA, DBM) has allowed to select the best management strategies, to be used as benchmark for real-time implementable control.Interdisciplinary research is needed, but also a systematic dissemination of results and potentialities, in order to remove the obstacles to the diffusion of such vehicles.


On-going activities

Development and implementation of real-time control strategies and comparison with benchmark solutions.

On road tests on the prototype to validate both simulation results and control strategies.

Installation of an automated sun-tracking roof to further enhance solar energy contribution.


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UL HSV – An artistic point of view

Thank you for your kind attention

Documents

Hybrid Solar Vehicles: Perspectives, Problems, Management Strategies