![R&D on Electrical & Electrochemical storage systems
ZEVA RTHEMISEMBOUTEILLAGE
Battery Pack Sizing Thermal Management laws
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Power/Energy requirements
or vehicle mission profiles
Batch Simulations with
cell constraints
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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
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3 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May](https://image.slidesharecdn.com/lms2011oralpradafinal-13317283545536-phpapp02-120314073643-phpapp02/75/Li-ion-into-EV-Simulator-LMS-Conference-2011-Oral-Presentation-3-2048.jpg)
![Model experimental calibration and validation
Experimental setup Calibration & software implementation
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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
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Experimental Data @ 0.5C Experimental Data Experimental data
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endothermic behaviours endothermic behaviours
9 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May](https://image.slidesharecdn.com/lms2011oralpradafinal-13317283545536-phpapp02-120314073643-phpapp02/75/Li-ion-into-EV-Simulator-LMS-Conference-2011-Oral-Presentation-9-2048.jpg)
Uploaded byEric Prada
Li-ion into EV Simulator - LMS Conference 2011 Oral Presentation
The document presents an innovative impedance-based 0D electro-thermal battery model developed by IFP Energies Nouvelles, focusing on its application in hybrid and electric vehicles. It details the model's calibration, integration into AMESIM software, and compares its performance against static models, highlighting its advantages in accurately predicting thermal behavior and battery performance under various conditions. The research emphasizes optimizing design and thermal management for enhanced efficiency in electric and hybrid vehicle applications.
Li-ion into EV Simulator - LMS Conference 2011 Oral Presentation
- 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 NouvellesR&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 © IFP New Energy 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] © IFP New Energy SOC SOH 3 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May
- 4. Introduction on multi-physicsbattery 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 © IFP New Energy 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 © IFP New Energy 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 © IFP New Energy 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 © IFP New Energy 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 modelin 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 © IFP New Energy 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 calibrationand 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 © IFP New Energy 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 versusDynamic 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 © IFP New Energy 10 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May
- 11. Classic static vsdynamic 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 © IFP New Energy 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 ofLi-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) © IFP New Energy 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 ofLi-ion Model Rev. 9a (2/2) 33.5° Hybrid Power Pulse Characterization @ 33.5°C A123 Li-ion Systems Cell Battery Voltage Prediction © IFP New Energy 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 onEV 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 © IFP New Energy 16 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May
- 17. Cases studies onEV 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 © IFP New Energy 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 © IFP New Energy 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 © IFP New Energy 19 Prada & al. – IFP Energies nouvelles – LMS Vehicle Conference 2011 – Munich -11-12th May