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Evaluation of EVs energy consumption influencing factors,

driving conditions, auxiliaries use, driver's aggressiveness

F. Badin1, F. Le Berr2, H. Briki3, J-C. Dabadie2, M. Petit1, S. Magand2, E. Condemine2

1 : IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3 69360 Solaize,

France (francois.badin@ifpen.fr)

2 : IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison,

France

3 : D2T, 11 rue Denis Papin, CS 70533, 78190 Trappes, France

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Problematic

• EV’s enable to avoid local nuisances

• EV’s energy consumption is very sensitive :

– To the vehicle range (driver’s anxiety)

– To the evaluation of the Life Cycle emission values

(GHG balance)

– To the evaluation of TCO

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Car manufacturers are very

sensitive to that point (EVs)

Driving

schedule

A/C use

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Car manufacturers are very

sensitive to that point (PHEVs)

Driving

schedule

A/C use

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Research program to evaluate the

influence of driving conditions and

ambient temperature

• IFPEN : Research center

• French ADEME : Financial support

• PSA, Renault and Tazzari : Technical support

• Three steps program :

– Measurements on 4WD climatic chassis dyno

– Validated EVs software

– Analytic correlation of Evs consumption

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Chassis dyno measurements

• Expensive tool ;

• Limited number of tests (6

cycles x 3 ambient cond.);

• Confidentiality (chassis

setting, CAN...) ;

• Not a benchmarking (3 EVs tested);

• Limited to existing vehicles and technologies

4WD climatic chassis dyno

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Simulation software (1/2)

• Dedicated library ;

• Complete set of data ;

• Validations (component & vehicle);

• Great care on the results ;

• Fast and not expensive ;

• Exhaustive analysis

LMS Imagine Lab AMESim

IFPEN EM test bench IFPEN BAT test bench

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Simulation software (2/2)

• Battery simulation case :

– Quasistatic model for short

power solicitations (cycling)

– Electrochemical model for long

solicitations (charging)

Session 7B M. Petit IFPEN

180

185

190

195

200

205

210

215

Time [s]

Pack v

oltage [V

]

Experimental data

Quasistatic model

Electroch. model

100 120 140 160 180 200

194

195

196

197

198

199

200

201

202

Pack v

oltage [V

]100 200 300 400 500 600

Time [s]

Experimental data

Quasistatic model

Electroch. model

10s HPPC 23°C

200s HPPC 23°C

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Software validation(1/2 component level)

0 200 400 600 800 1000 1200-150

-100

-50

0

50

100

150

Time [s]

Battery

current [A

]

Experiment

Simulation

0 100 200 300 400 500 600345

350

355

360

365

Time [s]

Battery

voltage [V]

Simulation

Experiment

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Software validation(2/2 vehicle level)

0

50

100

150

200

250

UL1 Artemis

Urbain

NEDC SC03 Hyzem Rural A1

Driving cycles

Energy consumption [Wh/km]

Simulation

Test bench

Average error < 4 %

Avg speed

< 4 km/h

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11

0 20 40 60 80 10080

100

120

140

160

180

200

220

240

Mean speed [km/h]

Energ

y c

onsum

ption [W

h/k

m]

Software resultsVehicle energy consumption

• Large number of driving

patterns (40*)

• Wide range of En Cons

90 to 240 Wh/km

• Disparities

* Influence of slope not considered

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0 2000 4000 6000 8000 100000

20

40

60

80

100

120

Distance [m]

Speed [km

/h]

Software resultsInfluence of driver’s aggressiveness (1/3)

• Dynamic analysis (NEDC cycle base)

Target speed

'Aggressive‘ driver

'Ordinary‘ driver

'Economic‘ driver

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‘Agressive’ driver

‘Economic’ driver

22,7 9,2

19,5 4,1

• Energy analysis

Software resultsInfluence of driver’s aggressiveness (2/3)

104

30,4Inertia

Aero.

Rr.

Recoverable

Brakes

111 Wh/km

Energy (wall)

Average speed

29,3 km/h

132 Wh/km

+20 % + stress

Average speed

35,9 km/h

Energy (wall)

+22 %

83,1Motoring

20,7RegenInert.

Aero.

Rr.

Rec.

Brakes

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-5C -4,5C -4C -3,5C -3C -2,5C -2C -1,5C -1C -0,5C 0 0,5C 1C 1,5C 2C 2,5C 3C0

100

200

300

400

500

600

700

800

Rated capacity [-]

Tim

e [s]

• Component stress analysis (battery)

Software resultsInfluence of driver’s aggressiveness (3/3)

‘Economic’ driver

‘Aggressive’ driver

Motoring Braking

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• Three cases tested,

250, 500 and 1000 W

Software resultsInfluence of auxiliary power

0 20 40 60 80 10050

100

150

200

250

300

350

400

450

Mean speed [km/h]

Energ

y c

onsum

ption [W

h/k

m]

1000W

500W

250W

+15% to 40% < +15%

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• Cons [Wh/km] =

K1aux + K2rolling + K3aero

+ K4acceleration

Analytical methodEvaluation of EV energy consumption

0 20 40 60 80 10080

100

120

140

160

180

200

220

240

Mean speed [km/h]

Energ

y c

onsum

ption [W

h/k

m] 'Aggressive‘ driver

'Ordinary‘ driver

'Economic‘ driver

+37 %

+15%

6,3)%1(

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)6,3()%1(

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6,3

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Conclusion

• Evaluation of EVs energy consumption thanks to a validated

software, on 40 driving patterns;

• Consumption ranges from 90 to 240 Wh/km (B segment);

• Influence of driver’s aggressiveness, up to 40% at 20 km/h and

up to 15 % at 60 km/h;

• Influence of auxiliaries, 15 to 40 % at 20 km/h and 5 to 15% at

60 km/h (resp. +250 and +750 W);

• Setting up of an analytic method to quickly evaluate EV

consumption with a reasonable error level;

• Work still in progress

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Thank you for your attention

Visit our booth B182

With the support of the French ADEME

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19

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Rotation speed [rev/min]

Torq

ue [Nm

]Cycle : Artemis Urban

100

90

80

70

60

50

40

30

20

10Electric motor characteristic

Proposed strategy

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0

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Rotation speed [rev/min]

Torq

ue [Nm

]Cycle : Hyzem Rural

100

90

80

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10Electric motor characteristic

Proposed strategy

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-150

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-50

0

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Rotattio speed [rev/min]

Torq

ue [Nm

]

Cycle : SC03

100

90

80

70

60

50

40

30

20

10Electric motor characteristic

Proposed strategy