Vppc12 oral-prada vf
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1. The document presents a physics-based model for lithium-ion battery aging during cycling and calendar modes. It predicts power and capacity fade by modeling solid electrolyte interface growth, lithium inventory loss, and modifications to the negative electrode porosity. 2. An experimental validation of the model is shown using impedance spectroscopy coupled with cycling tests. The model accurately predicts the measured power and capacity fade over time for different aging conditions. 3. Simulations using the model can help optimize battery management strategies for plug-in electric vehicles during storage to extend battery lifetime.
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VPPC 2012 – SEOUL - Prada & al. – October 10th
, 20123
©IFPNewEnergy
IFPEN R&D
Fuel efficient vehicles - Hybridization
LMS AMESim
Vehicle and
powertrain
simulation
3
TC sp
2
EC sp
1
To log ge r
TRANSMISSION
CONTRO L
D_Transmissi onMa nageme nt
ENGINE AND
TRANSMISSION
ACTUA TORS
D_Act uat ors_contr ol
ENGINE CONTRO L
C_Engi neMa na gement
VEHICLE
MANAGER
B_VehicleMa na ger
5
Activations
4
erro r
3
Sensors
2Calib data
1
Tri g
Real time
simulation
Energy storage
system
test bench
Engine test benches
BMS
Component
testing and
optimization
Integrated
powertrain
control
Vehicle
testing
Prototypes
design,
realization
and
optimization
A complete approach is developed
VPPC 2012 – SEOUL - Prada & al. – October 10th
, 20124
©IFPNewEnergy
SOC
SOH
I
U, T
IFPEN R&D
Electrochemical storage system models
0
5
10
15
20
25
30 0
50
100
150
-10
-5
0
SERIAL NUMBER
ZEV
A
RTHEMIS
E
MBOUTEILLAGE
PARALLEL NUMBER
0 1 00 2 0 0 30 0 4 0 0 50 0 6 00 7 0 0 80 0 9 00 1 0 00
-5 00 0
0
5 00 0
1 0 00 0
1 5 00 0
2 0 00 0
TIM E (s )
ELECTRICPOWER(W)
ZE V A RTHE M IS E M B O UTE IL L A G E
Battery and supercapacitors characterization
Multi-Physics & Multi-Dimensional Models
Electrochemical models,
Impedance-Based models
Aging
Thermal Management laws
Model Based BMS estimators SoC / SoHHEV / PHEV / EV Simulators
Battery Pack Sizing
Power/Energy requirements
or vehicle mission profiles
Batch Simulations with
cell constraints
& vehicle constraints
0 5000 10000 15000
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
SOC
Time [s]
Full Order Model
EKF estimation
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- 1. 1 Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources©IFPNewEnergy VPPC 2012 – SEOUL - Prada & al. – October 10th , 2012 PhysicsPhysicsPhysicsPhysics----based modeling of LiFePObased modeling of LiFePObased modeling of LiFePObased modeling of LiFePO4444----graphitegraphitegraphitegraphite LiLiLiLi----ion batteries for Power and Capacity fadeion batteries for Power and Capacity fadeion batteries for Power and Capacity fadeion batteries for Power and Capacity fade Predictions:Predictions:Predictions:Predictions: Application to calendar aging of (P)HEV and EVApplication to calendar aging of (P)HEV and EVApplication to calendar aging of (P)HEV and EVApplication to calendar aging of (P)HEV and EV E. PRADA, D. Di DOMENICO, Y. CREFF, J. BERNARD, V. SAUVANT-MOYNOT, and F. HUET* (from the LISE) VPPC 2012 – SEOUL - Prada & al. – October 10th , 20122 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives
- 2. 2 VPPC 2012 – SEOUL - Prada & al. – October 10th , 20123 ©IFPNewEnergy IFPEN R&D Fuel efficient vehicles - Hybridization LMS AMESim Vehicle and powertrain simulation 3 TC sp 2 EC sp 1 To log ge r TRANSMISSION CONTRO L D_Transmissi onMa nageme nt ENGINE AND TRANSMISSION ACTUA TORS D_Act uat ors_contr ol ENGINE CONTRO L C_Engi neMa na gement VEHICLE MANAGER B_VehicleMa na ger 5 Activations 4 erro r 3 Sensors 2Calib data 1 Tri g Real time simulation Energy storage system test bench Engine test benches BMS Component testing and optimization Integrated powertrain control Vehicle testing Prototypes design, realization and optimization A complete approach is developed VPPC 2012 – SEOUL - Prada & al. – October 10th , 20124 ©IFPNewEnergy SOC SOH I U, T IFPEN R&D Electrochemical storage system models 0 5 10 15 20 25 30 0 50 100 150 -10 -5 0 SERIAL NUMBER ZEV A RTHEMIS E MBOUTEILLAGE PARALLEL NUMBER 0 1 00 2 0 0 30 0 4 0 0 50 0 6 00 7 0 0 80 0 9 00 1 0 00 -5 00 0 0 5 00 0 1 0 00 0 1 5 00 0 2 0 00 0 TIM E (s ) ELECTRICPOWER(W) ZE V A RTHE M IS E M B O UTE IL L A G E Battery and supercapacitors characterization Multi-Physics & Multi-Dimensional Models Electrochemical models, Impedance-Based models Aging Thermal Management laws Model Based BMS estimators SoC / SoHHEV / PHEV / EV Simulators Battery Pack Sizing Power/Energy requirements or vehicle mission profiles Batch Simulations with cell constraints & vehicle constraints 0 5000 10000 15000 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 SOC Time [s] Full Order Model EKF estimation CC
- 3. 3 VPPC 2012 – SEOUL - Prada & al. – October 10th , 20125 ©IFPNewEnergy Industrial Context As LiAs LiAs LiAs Li----ion batteries are more and moreion batteries are more and moreion batteries are more and moreion batteries are more and more deployeddeployeddeployeddeployed forforforfor transportation applications,transportation applications,transportation applications,transportation applications, improvingimprovingimprovingimproving lifetimelifetimelifetimelifetime andandandand reliabilityreliabilityreliabilityreliability isisisis essential.essential.essential.essential. LiLiLiLi----ionionionion systemssystemssystemssystems lifetimelifetimelifetimelifetime predictionpredictionpredictionprediction isisisis stillstillstillstill an issue foran issue foran issue foran issue for batterybatterybatterybattery engineersengineersengineersengineers andandandand automotiveautomotiveautomotiveautomotive makersmakersmakersmakers becausebecausebecausebecause of multipleof multipleof multipleof multiple agingagingagingaging mechanismsmechanismsmechanismsmechanisms and multipleand multipleand multipleand multiple chemistrieschemistrieschemistrieschemistries (NCA, LFP,NMC,C,LTO...)!(NCA, LFP,NMC,C,LTO...)!(NCA, LFP,NMC,C,LTO...)!(NCA, LFP,NMC,C,LTO...)! Illustrations PSA & Renault Cars VPPC 2012 – SEOUL - Prada & al. – October 10th , 20126 ©IFPNewEnergy How toHow toHow toHow to definedefinedefinedefine LiLiLiLi----ion batteriesion batteriesion batteriesion batteries agingagingagingaging? (1/2)? (1/2)? (1/2)? (1/2) Batteries areBatteries areBatteries areBatteries are characterizedcharacterizedcharacterizedcharacterized bybybyby twotwotwotwo macroscopicmacroscopicmacroscopicmacroscopic parametersparametersparametersparameters :::: InternalInternalInternalInternal Resistance R (Resistance R (Resistance R (Resistance R (ΩΩΩΩ)))) PowerPowerPowerPower (W)(W)(W)(W) NominalNominalNominalNominal CapacityCapacityCapacityCapacity CCCCnomnomnomnom (Ah)(Ah)(Ah)(Ah) EnergyEnergyEnergyEnergy (Wh)(Wh)(Wh)(Wh) BatteryBatteryBatteryBattery agingagingagingaging leadsleadsleadsleads to ato ato ato a degradationdegradationdegradationdegradation of theof theof theof the system performances :system performances :system performances :system performances : Rise of Resistance RRise of Resistance RRise of Resistance RRise of Resistance R PowerPowerPowerPower losslosslossloss ReductionReductionReductionReduction ofofofof cellcellcellcell capacitycapacitycapacitycapacity CCCC EnergyEnergyEnergyEnergy losslosslossloss EndEndEndEnd----ofofofof----LifeLifeLifeLife (EOL)(EOL)(EOL)(EOL) isisisis oftenoftenoftenoften defineddefineddefineddefined whenwhenwhenwhen thethethethe residualresidualresidualresidual capacitycapacitycapacitycapacity CCCC isisisis 80% of80% of80% of80% of CCCCnomnomnomnom Spotnitz et al. , JPS 72 (2006) 113 EOL criterion # Closs = -20% Cnom CapacityCapacityCapacityCapacity fadefadefadefade curvecurvecurvecurve isisisis impactedimpactedimpactedimpacted by operating stressby operating stressby operating stressby operating stress factorsfactorsfactorsfactors (T,(T,(T,(T, ΔΔΔΔDOD, SOC, I)DOD, SOC, I)DOD, SOC, I)DOD, SOC, I) tEOL
- 4. 4 VPPC 2012 – SEOUL - Prada & al. – October 10th , 20127 ©IFPNewEnergy As aAs aAs aAs a functionfunctionfunctionfunction ofofofof batterybatterybatterybattery utilizationutilizationutilizationutilization in anin anin anin an electrifiedelectrifiedelectrifiedelectrified vehiclevehiclevehiclevehicle, one, one, one, one cancancancan distinguishdistinguishdistinguishdistinguish twotwotwotwo agingagingagingaging modes :modes :modes :modes : CYCLING & CALENDAR distribution for different vehicles (Source, Magna) CYCLING & CALENDAR modes are combined during the lifetime for electrified vehicles CYCLING ModeCYCLING ModeCYCLING ModeCYCLING Mode BatteryBatteryBatteryBattery isisisis cycledcycledcycledcycled in charge/in charge/in charge/in charge/dischargedischargedischargedischarge (I=(I=(I=(I=IIIIchchchch////dchdchdchdch)))) //// DrivingDrivingDrivingDriving &&&& ChargingChargingChargingCharging ModeModeModeMode CALENDAR ModeCALENDAR ModeCALENDAR ModeCALENDAR Mode BatteryBatteryBatteryBattery isisisis storedstoredstoredstored (I = 0 A)(I = 0 A)(I = 0 A)(I = 0 A) //// Parking ModeParking ModeParking ModeParking Mode How toHow toHow toHow to definedefinedefinedefine LiLiLiLi----ion batteriesion batteriesion batteriesion batteries agingagingagingaging? (2/2)? (2/2)? (2/2)? (2/2) VPPC 2012 – SEOUL - Prada & al. – October 10th , 20128 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives
- 5. 5 VPPC 2012 – SEOUL - Prada & al. – October 10th , 20129 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives VPPC 2012 – SEOUL - Prada & al. – October 10th , 201210 ©IFPNewEnergy Modified SP Electro-thermal model* ( )nrevirrev p skin qQQ mCdt dT −+= 1 cellcell cellconv cooling cellcell cellconv skincentre r Sh T r Sh TT λλ − += 1 ( )I dT UUd TQ np skinrev − −= ( )IUUVQ npirrev )( −−−= ( )coolingskincellconvn TTShq −= Energy Balance Irreversible Thermal power (W) Reversible Thermal power (W) Exchange Thermal power (W) Internal temperature (K) Thermal sub-model State of Charge of the (+) and (-)electrodes (%) Electrochemical sub-model ( ) SEI eff p p eff sep sep eff n n e e nnns ns nnns ns ppps ps ppps ps ns s ns n ps s ps p IR A I c Lc F RT t I Ai R I Ai R I Ai R I Ai R F RT c c U c c UtV − ++−−+ + + + + − + − = + κ δ κ δ κ δ δεδε δεδε α 2 2)0( )( ln 2 1 1 66 1 66 ln )( 2 ,0, , ,0, , 2 ,0, , ,0, , max,, , max,, , max,, , ps s pss p c c =θ max,, , ns s nss n c c =θ − − ×= b n b n b n b n batSOC %0,%100, %0, 100 θθ θθ State of Charge of the battery (%) Cell Voltage (V) Hypotheses and modeling approach The cell voltage (V) is expressed: 1) as the sum of kinetics and mass transport overpotentials in the electrodes and electrolyte 2) as a function of the design parameters (Porosity, Electrodes thicknesses...) and physical properties of electrodes and electrolyte (Diffusion coefficients, Electrochemical kinetics...) *Hypotheses and Set of Equations can be found in Prada et al. JES 159 (9) A1508-A1519 (2012), "A Simplified Electrochemical and thermal model of LiFePO4-graphite Li-ion batteries for Fast Charge Applications" Modified SP Electro- Thermal model OUTPUT Cell Voltage (V) SOCbat (%) Tskin (K) Tcentre (K) A123 Systems Cell, 2,3 Ah
- 6. 6 VPPC 2012 – SEOUL - Prada & al. – October 10th , 201211 ©IFPNewEnergy Electrochemical and Thermal Aging model Hypotheses and modeling approach 1) Reduction of Solvent molecules at the graphite/electrolyte interface 2) Mechanism of SEI layer growth & porosity modification of the negative electrode 3) Loss of cyclable Lithium ions (LCL) 4) No Loss of active material (LAM) at the positive and negative electrodes Equations of the aging model Electrochemical Reduction of Solvent Molecules (S) PLieS →++ +− 22 ( ) −−−−= nSEI SEI snegsolventfs S I U RT F CTFki κ δ φ β exp* Electrochemical kinetics (Tafel Formalism) sns iSQ dt d =Cyclable Lithium consumption SEI SEIs SEI F Mi dt d ρ δ 2 −=SEI Layer thickness growth rate solventSEIsolventsolventsolvent C rdt d C r DC t ∂ ∂ − ∂ ∂ = ∂ ∂ δ2 2 Transport of Solvent molecules within the SEI layer bulk solventSEIRrsolvent CC SEIns εδ =+= , F i C dt d C r D s RrsolventSEI Rr solventsolvent ns ns =+ ∂ ∂ − = = , , δ Boundary condition *) Boundary condition **) * ** SEI layer resistance (ΩΩΩΩ) Reduction of the effective diffusivity of the electrolyte in the negative electrode (m²/s) Reduction of the effective ionic conductivity of the electrolyte (S/m) Increase of ROhm (ΩΩΩΩ) Impact of SEI growth & Porosity modification on Power loss of the system nSEISEI SEIs SEI nSEI SEI SF Mi dt d S R dt d κρ δ κ 1 2 1 −== ( ) nBrugg ns SEI nsnf nBrugg ne eff n R t , , ,, , , 3 11 +−−== δ εεκκεκ ( ) ( ) ( ) ++ +−− = eff p p eff sep sep nBrugg ns SEI nsnfe n Ohm R t c A tR κ δ κ δ δ εεκ δ 2 3 11 2 1 , , ,, ( ) nBrugg ns SEI nsnfne nBrugg nene eff ne R t DDD , , ,,, , ,,, 3 11 +−−== δ εεε ( ) ( ) +−−= ns SEI nsnfne R t t , ,,, 3 11 δ εεεPorosity evolution of the negative electrode 3 1 1 , , , ns ns nfc SEI R − − = ε ε δ SEI Critical thickness corresponding to a complete filling of the porosity S e- Li+ Growth TheoreticalTheoreticalTheoreticalTheoretical correlationscorrelationscorrelationscorrelations betweenbetweenbetweenbetween Power andPower andPower andPower and CapacityCapacityCapacityCapacity FadeFadeFadeFade P VPPC 2012 – SEOUL - Prada & al. – October 10th , 201212 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives
- 7. 7 VPPC 2012 – SEOUL - Prada & al. – October 10th , 201213 ©IFPNewEnergy Experimental Set-up and Methods for the validation of Power and Capacity fade correlations Experimental setup Calendar Aging Tests: Endurance tests were performed at 80% SOC and 50°°°°C in a climatic chamber for 154 days Check-Up: Check-Up Procedure performed every 15 days consisting in : * 1C Charge/Discharge tests to determine the residual capacity at 25°°°°C (Capacity Fade) * Impedance characterizations at different SOC at 25°°°°C to follow the impedance increase of the cell (Power Fade) Electrochemical Impedance Spectroscopy withElectrochemical Impedance Spectroscopy withElectrochemical Impedance Spectroscopy withElectrochemical Impedance Spectroscopy with MultipotentiostatMultipotentiostatMultipotentiostatMultipotentiostat & climatic chambers& climatic chambers& climatic chambers& climatic chambers Post Mortem Analysis: Cell opening (Glove-Box), Materials analysis (MEB/DRX) (-) Electrode (+) Electrode Argon filled glove boxes to perform the cellArgon filled glove boxes to perform the cellArgon filled glove boxes to perform the cellArgon filled glove boxes to perform the cell opening and to build halfopening and to build halfopening and to build halfopening and to build half----cells based on recoveredcells based on recoveredcells based on recoveredcells based on recovered electrodeselectrodeselectrodeselectrodes A123 Systems Cell VPPC 2012 – SEOUL - Prada & al. – October 10th , 201214 ©IFPNewEnergy Interpretation of the EIS data during the aging test *EEC = Equivalent Electric Circuit*EEC = Equivalent Electric Circuit*EEC = Equivalent Electric Circuit*EEC = Equivalent Electric Circuit Rohm RSC RW CPE2CPE1 L Interpretation of the EIS DataImpedance Spectrum / EEC* Fitting method to extract parameters for each CU ( ) ( ) ( ) ++ +−− = eff p p eff sep sep nBrugg ns SEI nsnfe n Ohm R t c A tR κ δ κ δ δ εεκ δ 2 3 11 2 1 , , ,, Ohmic Resistance = ROhm As the SEI thickess δδδδSEI increases, ROhm increases and the Spectrum moves towards the right Hyp & Assumption: σσσσs,p & σσσσs,n high Re-,n & Re-,p= 0 ΩΩΩΩ nSEI SEI nnpp SEI n ct p ctSC SSiF TR SiF TR RRRR κ δ ++=++= ,0,0 pp p ct SiF TR R ,0 = nn n ct SiF TR R ,0 = Charge -Transfer Resistances at the (+) & (-) electrodes As the SEI thickess δδδδSEI increases, RSC increases and the Spectrum dilates Semi-Circle Resistance = RSC Hyp & Assumption: RSLI,p = 0 ΩΩΩΩ Hyp : i0,n = 6.5 A/m²
- 8. 8 VPPC 2012 – SEOUL - Prada & al. – October 10th , 201215 ©IFPNewEnergy Power and Capacity Fade Validation for the calendar Mode Comparison Between Model Predictions and Experimental data a) Predicted residual capacity compared to experimental data b) Predicted SEI Thickness c) Predicted Rohm increase compared to experimental data d) Predicted RSC increase compared to experimental data Validation ofValidation ofValidation ofValidation of theoreticaltheoreticaltheoreticaltheoretical correlationscorrelationscorrelationscorrelations betweenbetweenbetweenbetween Power andPower andPower andPower and CapacityCapacityCapacityCapacity FadeFadeFadeFade Results obtained with the ALIDISSI project data VPPC 2012 – SEOUL - Prada & al. – October 10th , 201216 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives
- 9. 9 VPPC 2012 – SEOUL - Prada & al. – October 10th , 201217 ©IFPNewEnergy Simulation study : Calendar Life Predictions * 10-year storage period is simulated with real temperature data from 3 cities in the world namely Dubai, Paris and Moscow. * Simulations are performed from 10% to 100% SOC of the battery Impact of T and SOC on the Battery Lifetime: TheTheTheThe higherhigherhigherhigher thethethethe temperaturetemperaturetemperaturetemperature and the SOC, Theand the SOC, Theand the SOC, Theand the SOC, The higherhigherhigherhigher thethethethe capacitycapacitycapacitycapacity losslosslossloss of theof theof theof the batterybatterybatterybattery BMSBMSBMSBMS StrategyStrategyStrategyStrategy duringduringduringduring storagestoragestoragestorage Tmean=27.3°°°°C Tmean=11.9°°°°C Tmean=5.9°°°°C VPPC 2012 – SEOUL - Prada & al. – October 10th , 201218 ©IFPNewEnergy OUTLINE I. Physics-based electrochemical and thermal aging model I.1 Modified Single-Particle Electro-thermal Model : Hypotheses & Main Equations I.2 Physics-based Electrochemical and thermal Aging Model : Hypotheses, Equations & Theoretical Correlations between Power and Capacity Fade II. Model experimental validation for Power and Capacity Fade II.1 Experimental Devices and Methods (EIS coupled with endurance tests) II.2 Interpretation of EIS data during the aging test II.3 Power and Capacity fade validation for CALENDAR mode III. Simulations study: Towards Battery management Strategies of (P)HEV and EV battery pack during storage IV. Conclusions and Perspectives
- 10. 10 VPPC 2012 – SEOUL - Prada & al. – October 10th , 201219 ©IFPNewEnergy Conclusions & Perspectives A simplified electrochemical and thermal aging model was developed for a commercial LiFePO4/C technology. The model integrates the SEI growth mechanism at the negative electrode and the modification of the porosity of this electrode. Theoretical correlations between power and capacity fade were developed and validated based on calendar tests performed at IFPEN. A Simulation study was performed to investigate the power and capacity fade for the calendar mode in three cities of the world (Paris, Moscow, Dubai) BMS strategies could be proposed to mitigate the calendar aging of PHEV and EV. Work in progress on battery management strategies issues for cycling operating conditions VPPC 2012 – SEOUL - Prada & al. – October 10th , 201220 ©IFPNewEnergy Thank you very much for your attention Questions