Application of electro-thermal Li-ion cell models to investigate battery pack sizing into EV simulators

1. Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources
http://www.vppc2010.org/
Impedance-
Impedance-based Li-ion modeling
Li-
for HEV / PHEV's
Eric PRADA, Fabrice LE BERR, Jean-Charles DABADIE,
Julien BERNARD, Rémy MINGANT, Valérie SAUVANT-MOYNOT
© IFP New Energy
IFP Energies nouvelles, France
Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

2. IFP Energies Nouvelles R&D
Fuel efficient vehicles - Hybridization
A complete and coordinated approach
Vehicle and Prototypes
powertrain design,
simulation Real time Vehicle realization
Integrated Component testing and
simulation
powertrain testing, modeling & optimization
control optimization
LMS AMESim
Energy storage
system
test bench
1
Trig
2
EC sp
BMS
1
To logger
ENGINE CONT ROL
3
Sensors VEHICLE
M AN AGER
4
B_VehicleManager C_EngineManagement ENGINE AND
error TR ANSM ISSION
ACTU ATORS
2
Calib data
5
Activations
TR ANSM ISSION
CONTR OL
D_Actuat ors_co ntrol
3
TC sp
D_Transmission Management
Engine test benches
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2 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

3. R&D on Electrical & Electrochemical storage systems
ZEVA RTHEMISEMBOUTEILLAGE
Battery Pack Sizing Thermal Management laws
ZE V A R TH E M IS E M B O U TE ILLA G E
20000
0
15000
ELECTRIC POWER (W)
-5
10000
5000
-10
0
5 150
0 10
15 100
20
-5000 50
0 100 200 300 4 00 500 600 700 8 00 9 00 1000 25
TIM E (s )
30 0 SERIAL NUMBER
PARALLEL NUMBER
Power/Energy requirements
or vehicle mission profiles
Batch Simulations with
cell constraints
& vehicle constraints
BATTERIES and SUPERCAPACITORS characterization
Multi Physics & Multi Dimensional Models
Electrochemical models, Impedance-Based models
AGEING & THERMAL RUNAWAY mechanisms
HEV / PHEV / EV Simulators Model Based BMS estimators SoC / SoH
0.8
U, T
Full Order Model
EKF estimation
0.6 CC
0.4
0.2
SOC
0
-0.2
-0.4
-0.6
0 5000 10000 15000
I Time [s]
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SOC
SOH
3 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

4. Introduction on multi-physics battery models
Batteries are complex electrochemical energy storage systems (ESS). An accurate
knowledge, through refined modeling, is crucial to develop safe, optimized, more
durable and cost efficient energy storage systems for cleaner transportation
ELECTRICAL THERMAL
Refined energy
Refined Impedance- balance
based model... Thermal runaway...
ESS MULTI-PHYSICS
THERMAL/ELECTRICAL/CHEMICAL
MODELS
0D to 3D
CHEMICAL
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Concentration
modelled to know
species insertion at
each time
4 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

5. OUTLINE
I. Impedance-based 0D electro-thermal battery model
I.1 0D electrical / electrochemical model
I.2 0D thermal model
I.3 Experimental model calibration and validation
I.4 Classic Static vs Dynamic model on HEV profile
II. Battery Model Integration on AMESim Software
III. Cases studies on EV architecture : Static vs Dynamic
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IV. Conclusion
5 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

6. OUTLINE
I. Impedance-based 0D electro-thermal battery model
I.1 0D electrical / electrochemical model
I.2 0D thermal model
I.3 Experimental model calibration and validation
I.4 Classic Static vs Dynamic model on HEV profile
II. Battery Model Integration on AMESim Software
III. Cases studies on EV architecture : Static vs Dynamic
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IV. Conclusion
6 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

7. 0D electrical/electrochemical model
Kuhn & al. JPS 158 (2006) 1490-1497
Thermodynamic equilibrium potential(s) - OCV
Redlich-Kister law accounting for
RT  1 − x  N Ak  2 xk (1 − x ) 
+∑ (2 x − 1) −
k +1
U th = U th +
0
ln  complex solid solutions where x
F  x  k =0 F  (2 x − 1)1− k 
 represents Li inserted in electrodes
Charge transfer and electrochemical kinetics non-linearity Randles Electrical Circuit of the cell
Hypothesis of charge transfer
symmetry αox=αred=0.5
−1
 ∂ if  RT
R ct = 
 ∂η 
 =
   if 
2
i 0 nF 1+ 
 2i 

 0 
Diffusion of ionic species Concentration Impedance in the frequencial
domain can be easily implemented in the
tanh sτ temporal one thanks to Mittag-Leffler
Z (s) = R D
Typical Nyqvist Impedance diagram
sτ
w d
D Theorem and Foster and Cauer networks
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U = U th + η Ω + η ct + η conc
Foster Structure Cauer Structure
7 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

8. 0D Thermal model in nominal conditions
Global Energy balance Electric Power Loss
Qtotal is the sum of electric
∂T power generated during
mC = Q total − q n operation, possible short Qelec , gen = Qirrev + Qrev
∂t
p
and decomposition
reaction...
Qtotal = Qelec , gen + Qabuse + Qshort Electric power loss is the sum of
Cp is the calorific capacity IRREVERSIBLE and REVERSIBLE
q n = Acell h(T − Ta mb ) of the cell (J/kg/K) contributions
Irreversible Heat (Joule Effect) Reversible Heat (Entropic effect)
0.2
I dUth/dT
Qirrev = Z i I ² Q rev = − T ∆ S 0.1
nF
dU th /dT (mV /K )
0
In classic battery
models, only this
∂U th -0.1
irreversible ∆S = −nF (SOC)
contribution is ∂T -0.2
considered Chemical Reactions
-0.3
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0 20 40 60 80 100
SOC (%)
Qirrev is always exothermic (>0) Qrev can be exothermic (>0) or endothermic (<0)
8 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

9. Model experimental calibration and validation
Experimental setup Calibration & software implementation
x=
[
SOC
V1
V2
T]
1
dxdt
s
V Vcell
Time
fcn To Workspace
Clock X
To Workspace1
SoC SoC
To Workspace 2
[tps_courant courant ]
From Tpeau Tpeau
Workspace
To Workspace 3
Champ
Electrochemical Impedance High power test bench Electrochemical Impedance Spectroscopy
Model implementation on
Spectroscopy with Multipotentiostat & 50V-200A, Charge/discharge, spectra as a function of SOC
Matlab/Simulink and AMESim
climatic chambers HPPC Tests + Pulses testing at high current
A123 Systems 2,3Ah cell Prada & al. VPPC (2010) Lille
Validation with CONTINUOUS currents Validation with DYNAMIC HPPC
38 4 35.2
Experimental Data @ 0.5C Experimental Data Experimental data
3.4 Cell Voltage (V) 37.5 Model Prediction @ 0.5C
Cell Voltage (V) Model Prediction 35 Model Prediction
Experimental Data @ 1C
3.3 37 Model Prediction @ 1C 34.8
Experimental Data @ 2C
36.5 34.6
S u rfa c e T e m p e ra tu re (°C )
S urfac e T em perature (°C )
Model Prediction @ 2C 3.5
3.2 C e ll V olta g e (V )
C e ll V olta g e (V )
36 34.4
3.1
°
Skin Temperature (°C)
2C 1C 0.5C 35.5 34.2
3 35 34
3
Experimental Data @ 0.5C
34.5 33.8
2.9 Model Prediction @ 0.5 C
Experimental Data @ 1C
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34 33.6
Model Prediction @ 1C
°
2.8
Experimental Data @ 2C 33.5 33.4 Skin Temperature (°C)
Model Prediction@ 2C 2.5
2.7 33 33.2
0 1000 2000 3000 4000 5000 6000 7000 0 1000 2000 3000 4000 5000 6000 7000 0 2000 4000 6000 8000 10000 12000 14000 16000 0 2000 4000 6000 8000 10000 12000 14000 16000
Tme (s) Time (s) Time (s) Time (s)
0.5C, 1C, 2C Discharges rates @ 33°C with exo & 10C/10s-pulse tests @ 33°C with exo &
endothermic behaviours endothermic behaviours
9 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

10. Classic Static versus Dynamic model
Static models are mainly used in electrified
vehicles simulators since these are easy and
quick to calibrate but not accurate.
UNDER ESTIMATION
or
OVER ESTIMATION
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10 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

11. Classic static vs dynamic model on HEV profile
HEV real duty cycle tested at IFPEN Simulations Results
Voltage Error
40 Mean Error (%) 17
vs Data
18
Maximal Error (%)
20 16
3.5
14
VOLTAGE (V)
CURRENT (A)
0
12
-20 10
3
8 5
-40 6
Dynamic
2.5 4
-60 Static
|Imean|=7.5A Data 2
0,42 1,35
Maximal Error (%)
-80 0
Mean Error (%)
8000 8200 8400 8600 8800 9000 9200 9400 8000 8200 8400 8600 8800 9000 9200 9400
TIME (s) TIME (s) DYNAMIC MODEL
STATIC MODEL
4 Temperature
20 Mean Error (%)
4 Error vs Data
50 Static Maximal Error (%)
Dynamic 3,5
SKIN TEMPERATURE (°C)
45
VOLTAGE ERROR (%)
15
3
40
2,5
35 10
2 1,22
30
1,5
Dynamic 5 1,25
25 1
Static
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20 0,5 0,38 Maximal Error (%)
Data
0
8000 8200 8400 8600 8800 9000 9200 9400 8000 8200 8400 8600 8800 9000 9200 9400 0
Mean Error (%)
TIME (s) TIME (s) DYNAMIC MODEL
STATIC MODEL
11 Important differences on high power pulses that impact model-based (P)HEV sizing model-
Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

12. OUTLINE
I. Impedance-based 0D electro-thermal battery model
I.1 0D electrical / electrochemical model
I.2 0D thermal model
I.3 Experimental model calibration and validation
I.4 Classic Static vs Dynamic model on HEV profile
II. Battery Model Integration on AMESim Software
III. Cases studies on EV architecture : Static vs Dynamic
© IFP New Energy
IV. Conclusion
12 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

13. AMESim Implementation of Li-ion Model Rev. 9a (1/2)
Battery model on AMESim BATTERY PACK ICON :
2 electrical ports Current)
(Voltage and Current)
2 signal ports (SOC and OCV)
1 thermal port Losses)
(Thermal Power Losses)
ARCHITECTURE PARAMETERS
series)
Ns (Number of cells in series)
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parallele)
Np (Number of chain in parallele)
The user can design electrical pack architecture to optimize the
13
system according to vehicle Power/Energy requirements
storage Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May
Power/Energy

14. AMESim Implementation of Li-ion Model Rev. 9a (2/2)
33.5°
Hybrid Power Pulse Characterization @ 33.5°C
A123 Li-ion
Systems Cell
Battery Voltage Prediction
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Battery SOC Prediction Battery skin Temperature Prediction
14 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

15. OUTLINE
I. Impedance-based 0D electro-thermal battery model
I.1 0D electrical / electrochemical model
I.2 0D thermal model
I.3 Experimental model calibration and validation
I.4 Classic Static vs Dynamic model on HEV profile
II. Battery Model Integration on AMESim Software
III. Cases studies on EV architecture : Static vs Dynamic
© IFP New Energy
IV. Conclusion
15 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

16. Cases studies on EV architecture (1/2) (Rev. 9SL1)
Virtual electric vehicle simulator
VIRTUAL
VEHICLE COMPONENTS
Electric Motor
Power = 34 kW
Torque = 340 N.m
cells)
Battery Pack (A123 systems cells)
Nseries = 70
Nparallel = 30
Mass = 150 kg
20°
Cooling Air Temperature = 20°C
Vehicle
Mass = 940 kg
SCx = 0.8 m²
m²
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16 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

17. Cases studies on EV architecture (2/2) (Rev. 9SL1)
Evaluation on 12 successive
NEDC cycles (to use the whole
battery pack autonomy ca 3,9h)
|Imean|=0.5A
Comparison between classic
static model and IFPEN model on
Voltage profiles
CPU Time Voltage
0.26s for 1000 real sec (Static)
0.34s for 1000 real sec (Dynamic)
Skin Temperature evolutions
between classic and IFPEN Model LY C
NG M I
RO ER
Little differences since the mean ST TH
EX
O
current through a cell is around SLIGHTLY
EXOTHERMIC
SLIGHTLY
ENDOTHERMIC
Imean|=0.5A
|Imean|=0.5A Advanced
model clearly shows different
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ways of managing system
temperature
17 Prada & al. – is a powerful tool to optimize 2011 – Munich -11-12th May
Advanced modelIFP Energies nouvelles – LMS Vehicle Conference systems thermal management laws

18. Conclusion
IFP Energies nouvelles has developed a modeling approach taking into
account Li-ion behaviors in nominal conditions to optimize pack design, sizing
and thermal specifications.
The 0D dynamic electro-thermal model was validated on different experimental
tests performed on IFPEN Power tests benches.
A gain quantification of the dynamic model with an optimized thermal balance
was established compared to classic static battery model
Advanced dynamic model is well adapted for HEV/PHEV pack power sizing
without strongly impacting on CPU Time
The model was integrated on AMESim Software and tested on HEV/PHEV and
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EV simulators
18 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May

19. Questions
Thank you very much for your attention
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19 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May