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Organized by Hosted by In collaboration with Supported by
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 ([email protected])
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
Organized by Hosted by In collaboration with Supported by
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
Organized by Hosted by In collaboration with Supported by
Car manufacturers are very
sensitive to that point (EVs)
Driving
schedule
A/C use
Organized by Hosted by In collaboration with Supported by
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
Organized by Hosted by In collaboration with Supported by
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
Organized by Hosted by In collaboration with Supported by
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(
.
)6,3()%1(
...2
1
6,3
.]/.[
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Organized by Hosted by In collaboration with Supported by
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
0 1000 2000 3000 4000 5000 6000 7000 8000-200
-150
-100
-50
0
50
100
150
200
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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20
0 1000 2000 3000 4000 5000 6000 7000 8000-200
-150
-100
-50
0
50
100
150
200
Rotation speed [rev/min]
Torq
ue [Nm
]Cycle : Hyzem Rural
100
90
80
70
60
50
40
30
20
10Electric motor characteristic
Proposed strategy
Organized by Hosted by In collaboration with Supported by
21
0 1000 2000 3000 4000 5000 6000 7000 8000-200
-150
-100
-50
0
50
100
150
200
Rotattio speed [rev/min]
Torq
ue [Nm
]
Cycle : SC03
100
90
80
70
60
50
40
30
20
10Electric motor characteristic
Proposed strategy