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08/07/2016
1
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
«EMR OF BATTERY AND TRACTION
SYSTEMS»
Nicolas Solis12, Luis Silva1 , Dr. Ronan German2,Pr. Alain Bouscayrol2
1 Université de Rio Cuarto, Argentina2 L2EP, Université Lille1, MEGEVH network, France
EMR’16, UdeS Longueuil, June 20162
« EMR of battery and traction systems»
- Outline -
1. Context of the presentation
• Description of the work
• Batteries in EV context
• Importance of temperature for battery
2. Battery modeling
• Electrical model of battery
• Thermal model of battery
• Coupling thermal and electrical domains by EMR
3. Simulation results
• Validation model with literature results
• Interest on temperature estimation (WLTC Cycle)
• Interest on SOC estimation (WLTC Cycle)
4. Conclusions
08/07/2016
2
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
« CONTEXT OF THE PRESENTATION »
EMR’16, UdeS Longueuil, June 20164
« EMR of battery and traction systems»
- Description of the work-
Goal of the work
Take into account temperature in battery models
Couple existing electric and thermal models
Method
Application
Energy management of batteries (in EV for example ….)
Ibat cyclesNormalized
speed cycles
UAC
WLTC
Tazzari Zero
model
M=542 kg
P= 14,5kW
LiFePo high
power cells model
Cbat=2.5 Ah
Ibat max= 20 C0 500 1000 1500-50
0
50
100
Time (s)
i ba
t (c-r
ate
)
08/07/2016
3
EMR’16, UdeS Longueuil, June 20165
« EMR of battery and traction systems»
- EV related definitions-
Electrical vehicles (EV)
• Less maintenance than ICE vehicles
• No direct emissions of CO2
• Higher efficiency (80%) compared to ICE vehicles (40%)
• Vehicles propulsed only by electric energy without the help of any ICE
EV strong points
ESS available for EV
Li-ion Batteries pack
Energy storage systems
• Store and give back energy
SC module Fuell cell + H2 tank Capacitors
The L2EP, Tazzari Zero
EMR’16, UdeS Longueuil, June 20166
« EMR of battery and traction systems»
- Batteries in EV context-
• Responsible of
• Cost
• Recharge time
• Autonomy of the vehicle
Comparison of different ESSs
100
102
104
106
10-2
100
102
104
Mass Power (W/kg)
Mas
s En
ergy
(W
h/k
g)
36 ms
1 h 36 s100 h
Fuell cell
SCs
Capacitors
Li-ion battery technology
• Energy density compatible
with 150 km autonomy for
standard EV
• Power density compatible
with EV acceleration
Batteries
Li-ion
Ni-MhPb
In most EV the battery is the main ESS,
Example of 14,5 kWh Li-ion pack
placed in theTazzari Zero
08/07/2016
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EMR’16, UdeS Longueuil, June 20167
« EMR of battery and traction systems»
- Importance of temperature for battery-
i
e-e-
Electrodes
SeparatorElectrolyte
Solid Lithium Ionic Lithium
e- Electrons flow
Energy storage principle
Instant battery electric parameters
variation
• Increase of Electrolyte viscosity
with lower temperature
Ageing acceleration factor
Catastrophic fails
Triple temperature effect on batteries
• Fast breakdowns for out of bounds
temperature (Low or High)
Rbat dependent of T°
Cbat dependent of T°
EMR’16, UdeS Longueuil, June 20168
« EMR of battery and traction systems»
- Quantification of temperature impact on batteries-
Results on battery electric parameters variation with temperature*
[Lin et al 13]
0 20 40 60 800.005
0.01
0.015
0.02
0.025
0.03
Rbat
(mΩ)
T (ºC)
Cbat
(P.U)
[ Results extracted from
Jaguemont 14]
-20 0 20 40
0.8
1
1.2
1.4
T (ºC)
Temperature in battery modelling is very important
Results on battery ageing with temperature
*Results obtained for LiFePo batteries
+10 °C Life time reduced by half [ Edd 12]
08/07/2016
5
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
« BATTERY MODELLING »
EMR’16, UdeS Longueuil, June 201610
« EMR of battery and traction systems»
- Battery equations and EMR representation in electrical domain-
Usual Battery and traction EMR representation
• EMR representation
UOCV
ibat
Rbat
Ubat
Battery Converter of
EV
traction
Uconv
iconv
BattEV
Traction
Ubat
ibat
• Electrical domain only
Other possible battery EMR representation
• EMR representation
[Lin et al 2013]
OCV
storage
UOCV
ibatRbat
UbatUOCV
ibat
OCV
Storageibat
Ubat
[ Bouscayrol 12]
08/07/2016
6
EMR’16, UdeS Longueuil, June 201611
« EMR of battery and traction systems»
- Battery equations and EMR representation in thermal domain
Power loss in Rbat is the power heat source
[Lin et al 2013]
• Thermal domain modelling :
• Kinetic Variable=𝑞𝑠𝑥 (Entropic Flow)
• Potential Variable=𝑇𝑥 (Temperature)
Simplified EMR representation of the thermal domain of the
battery
Tcoren
qs1
Heat
Source
Heat Power
Source
Thermal
model
UOCV
ibat
Rbat
Ubat
Pheat = Pjoule= Rbat ibat2
Pheat=qs1Tcore
Pheat=qsxTx
EMR’16, UdeS Longueuil, June 201612
« EMR of battery and traction systems»
- Introduction to battery thermal modeling-
Thermal capacitance
Thermal energy storage
Thermal resistance
Selfheating as a function of the power transfert
Hypothesis• Heat source at the core center
• Radial conduction only in solid
• Convection only for solid to gas
heat transfer
• Only contact thermal resistance
taken into account
Package
Surface
Important notions
Heat
power
source
Tamb
Ccore Csurf
Rcond Rconv
Tsurf
Tamb
Pheat=RBat.IBat² TCore
Equivalent circuit
thermal model
Battery (1cell)
Core
Air
Tamb
Air
Tamb
[ Forgez 09] [Lin 13]
08/07/2016
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EMR’16, UdeS Longueuil, June 201613
« EMR of battery and traction systems»
- Battery equations and EMR representation in thermal domain-
Heat
power
source
Tamb
P2 P4
Ccore Csurf
Rcond Rconv
Tsurf
Tamb
Pheat=qs1Tcore
Equivalent thermal model(structural representation)
Electrical parameters are usually fixed
Ccore RconvRcond
Tcore
Tamb
EnvHeat
SourceTcore
qs1
qs3
qs4 qs7Tsurf
qs6
Csurf
Tsurf
Equivalent thermal model(EMR representation)
EMR’16, UdeS Longueuil, June 201614
« EMR of battery and traction systems»
- Coupling of electrical and thermal model of battery with EMR-
• Electrical domain
Ubat=UOCV - ibatRbat
• Thermal domain
Tcoren
qs1 Heat
Source
qs1=Pheat/TcorePheat= ibat2 Rbat
Ubat
Electro-thermal
coupling
UOCV
ibat
ibat
Tcore
qs1
EV
Traction
Thermal
EMR
OCV
Storage
Rbat
Rbat
Rbat in the coupling element in EMR
Rbat is the common element
Batt
UOCV
ibat
Ubat
ibat
08/07/2016
8
EMR’16, UdeS Longueuil, June 201615
« EMR of battery and traction systems»
- Coupling of battery in EMR-
Final thermo-electric battery representation
Worthy to have this complexity?
• Electrical and thermal models coupled with EMR
• With temperature dependent electrical parameters
𝐶𝐵𝑎𝑡 = 𝐶𝐵𝑎𝑡 0 + 𝐾𝐶 ∙ 𝑇𝐶𝑜𝑟𝑒𝑅𝐵𝑎𝑡 = 𝑅𝐵𝑎𝑡 0 ∙ 𝑒−𝑇𝐶𝑜𝑟𝑒𝑇0
UOCVOCV
storageibat
EV
Tractionibat
Ubat
Tcore
Tcore
Ccore
Tsurf Tamb
Env
RconvRcond
qs1
qs3
qs4 qs7Tsurf
qs6
Csurf
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
« SIMULATION RESULTS»
08/07/2016
9
EMR’16, UdeS Longueuil, June 201617
« EMR of battery and traction systems»
- Validation of the model with temperature dependence-
• Drive cycle: UAC
0 500 1000-20
0
20
Time (s)
i ba
t (c-r
ate
)
• Good dynamic
• Maximum error in
temperature=1,5 ºC
• Tamb=25 ºC
• Battery = A123 systems LiFePO4
• 2,5 Ah
• 3,3 V
• 20 C
Model validated
0 200 400 600 800 100025
30
35
40
45
50
55
Time (s)
Tem
pera
ture
(ºC
)
Experimental results
from [Lin et al 2013]
CoreSimulation
Surface
ExperimentWith temperature dependence
Experimental setup for validation
EMR’16, UdeS Longueuil, June 201618
« EMR of battery and traction systems»
- Classic and temperature dependent models comparison -
Necessity of thermal
depence model validated
• Tamb=25 ºC
• Battery modeled = A123 systems LiFePO4
• 2,5 Ah
• 3,3 V
• 20 C
0 500 1000 1500-50
0
50
100
Time (s)
i ba
t (c-r
ate
)
• Drive cycle: WLTC
• Maximum error in
temperature= 4 ºC
• Temperature over-estimated
Without temperature dependence
Temperature estimation (simulation)
0 500 1000 150025
30
35
40
Time (s)
Tem
pera
ture
(ºC
)
Core
Without T° dependence
With T° dependence
08/07/2016
10
EMR’16, UdeS Longueuil, June 201619
« EMR of battery and traction systems»
- Classic and temperature dependent models comparison -
Necessity of thermal
dependence model
validated
• Under-estimated SOC
• Maximum error in SOC = 8,4 %
0 500 1000 15000
20
40
60
80
100
Time (s)
SO
C (
%)
Without T° dependence
With T° dependence
0 500 1000 1500-50
0
50
100
Time (s)
i ba
t (c-r
ate
)
• Drive cycle: WLTC• Tamb=25 ºC
• Battery modeled = A123 systems LiFePO4
• 2,5 Ah
• 3,3 V
• 20 C
Without temperature dependence
SOC estimation (simulation)
EMR’16, UdeS Longueuil, June 201620
« EMR of battery and traction systems»
- Classic and temperature dependent models comparison -
0 500 1000 1500-50
0
50
100
Time (s)
i ba
t (c-r
ate
)
• Drive cycle: WLTC
• Battery modeled = A123 systems LiFePO4
• 2,5 Ah
• 3,3 V
• 20 C
Simulations at different temperatures
Errors on estimations without temperature dependence
Ambient T° (°C) 25 °C -20 °C
Max TCore
estimation error+4 °C -12 °C
Max SOC
estimation error-8.4 % + 10 %
08/07/2016
11
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
« Conclusions»
EMR’16, UdeS Longueuil, June 201622
« EMR of battery and traction systems»
Conclusions and perspectives
Temperature dependent on battery electrical parameters is necessary
• Better estimation of SOC
• Better estimation of temperature
Battery are the key component of the majority of EV
Battery physical principles explains T° dependence
EMR allows easy organization for coupling different physical domains
08/07/2016
12
EMR’16
UdeS - Longueuil
June 2016
Summer School EMR’16
“Energetic Macroscopic Representation”
« BIOGRAPHIES AND REFERENCES »
EMR’16, UdeS Longueuil, June 201624
« EMR of battery and traction systems»
- Authors -
Dr. Ronan German
University Lille 1, L2EP, MEGEVH, France
PhD in Electrical Engineering at University of Lyon (2013)
Research topics: Energy Storage Systems, EMR, HIL simulation,
EVs and HEVs
Angel Nicolas SOLIS
University Lille 1, L2EP, France
Ing in Electrical Engineering at Univ.National of Río
Cuarto (2014)
Research topics: EMR, EVs
08/07/2016
13
EMR’16, UdeS Longueuil, June 201625
« EMR of battery and traction systems»
Prof. Alain BOUSCAYROL
Université Lille 1, L2EP, MEGEVH, France
Coordinator of MEGEVH, French network on HEVs
PhD in Electrical Engineering at University of Toulouse (1995)
Research topics: EMR, HIL simulation, tractions systems, EVs and HEVs
- Authors -
Dr. Luis Silva
Universidad Nacional de Rio Cuarto, GEA, Argentina
PhD in Sciences of Engineering at UNRC (2012)
Research topics: EMR, Modeling and Simulation of
Electric and Hybrid Vehicles
EMR’16, UdeS Longueuil, June 201626
« EMR of battery and traction systems»
- References -
[Bouscayrol 12] A. Bouscayrol, J. P. Hautier, B. Lemaire-Semail, "Graphic formalism for the control of
multi-physical energetic systems", Systemic design methodologies for electrical energy, tome 1, Chapter 3,
ISTE Willey editions, October 2012, ISBN 9781848213883
[Lin 13] X. Lin, H. E. Perez, S. Mohan, J. B. Siegel, A. G. Stefanopoulou, Y. Ding, M. P. Castanier, “A
lumped-parameter electro-thermal model for cylindrical batteries”, Journal of Power Sources, Volume 257,
1 July 2014, Pages 1-11, ISSN 0378-7753.
[Jaguemont 14] J. Jaguemont, L. Boulon, Y. Dube and D. Poudrier, "Low Temperature Discharge Cycle
Tests for a Lithium Ion Cell“, 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), Coimbra,
2014, pp. 1-6.
[Dürr 06] Matthias Dürr, Andrew Cruden, Sinclair Gair, J.R. McDonald, Dynamic model of a lead acid
battery for use in a domestic fuel cell system, Journal of Power Sources, Volume 161, Issue 2, 27 October
2006, Pages 1400-1411, ISSN 0378-7753, http://dx.doi.org/10.1016/j.jpowsour.2005.12.075.
[Forgez 09] Christophe Forgez, Dinh Vinh Do, Guy Friedrich, Mathieu Morcrette, Charles Delacourt,
Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery, Journal of Power Sources, Volume
195, Issue 9, 1 May 2010, Pages 2961-2968, ISSN 0378-7753,
http://dx.doi.org/10.1016/j.jpowsour.2009.10.105.
[Edd 12] A. Eddahech, O. Briat, E. Woirgard, J.M. Vinassa, Remaining useful life prediction of lithium
batteries in calendar ageing for automotive applications, Microelectronics Reliability, Volume 52, Issues 9–
10, September–October 2012, Pages 2438-2442.
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