«EMR OF BATTERY AND TRACTION SYSTEMS»

Transcription

1 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «EMR OF BATTERY AND TRACTION SYSTEMS» Nicolas Solis 12, Luis Silva 1, Dr. Ronan German 2,Pr. Alain Bouscayrol 2 1 Université de Rio Cuarto, Argentina 2 L2EP, Université Lille1, MEGEVH network, France - Outline 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 1

2 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «CONTEXT OF THE PRESENTATION» - Description of the work- 4 Goal of the work Take into account temperature in battery models Couple existing electric and thermal models Application Energy management of batteries (in EV for example.) Method Normalized speed cycles Tazzari Zero model 100 I bat cycles LiFePo high power cells model UAC WLTC M=542 kg P= 14,5kW (c-rate) C bat =2.5 Ah I bat max = 20 C 2

3 - EV related definitions- Electrical vehicles (EV) Vehicles propulsed only by electric energy without the help of any ICE 5 EV strong points Less maintenance than ICE vehicles No direct emissions of CO 2 Higher efficiency (80%) compared to ICE vehicles (40%) Energy storage systems Store and give back energy The L2EP, Tazzari Zero ESS available for EV Li-ion Batteries pack SC module Fuell cell + H 2 tank Capacitors Comparison of different ESSs - Batteries in EV context- 6 Mass Energy (Wh/kg) Fuell cell 100 h Pb Li-ion Ni-Mh Batteries SCs 1 h 36 s Capacitors Mass Power (W/kg) 36 ms In most EV the battery is the main ESS, Li-ion battery technology Energy density compatible with 150 km autonomy for standard EV Power density compatible with EV acceleration Responsible of Cost Recharge time Autonomy of the vehicle Example of 14,5 kwh Li-ion pack placed in thetazzari Zero 3

4 - Importance of temperature for battery- 7 Energy storage principle Triple temperature effect on batteries e - i Electrodes e - Instant battery electric parameters variation Increase of Electrolyte viscosity with lower temperature R bat dependent of T C bat dependent of T Ageing acceleration factor Separator Electrolyte Solid Lithium Ionic Lithium e - Electrons flow Catastrophic fails Fast breakdowns for out of bounds temperature (Low or High) - Quantification of temperature impact on batteries- 8 Results on battery electric parameters variation with temperature* *Results obtained for LiFePo batteries R bat (mω) C bat (P.U) T (ºC) [Lin et al 13] Results on battery ageing with temperature +10 C Life time reduced by half [ Edd 12] T (ºC) [ Results extracted from Jaguemont 14] Temperature in battery modelling is very important 4

5 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «BATTERY MODELLING» - Battery equations and EMR representation in electrical domain- Usual Battery and traction EMR representation Battery Rbat i conv Converter of EV traction U conv [ Bouscayrol 12] EMR representation Batt EV Traction 10 Other possible battery EMR representation OCV storage R bat EMR representation [Lin et al 2013] OCV Storage Electrical domain only 5

6 - Battery equations and EMR representation in thermal domain Power loss in R bat is the power heat source 11 Heat Power Source R bat P heat = P joule = R bat 2 [Lin et al 2013] Thermal domain modelling : Kinetic Variable=q sx (Entropic Flow) Potential Variable=T x (Temperature) P heat =q sx T x Simplified EMR representation of the thermal domain of the battery Heat Source q s1 T coren Thermal model P heat =q s1 T core Important notions Thermal capacitance Thermal energy storage 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 - Introduction to battery thermal modeling- Thermal resistance Selfheating as a function of the power transfert Air Battery (1cell) Core [ Forgez 09] [Lin 13] Package Surface Air 12 P heat =R Bat.I Bat ² T Core T surf Equivalent circuit thermal model Heat power source C core R cond C surf R conv 6

7 - Battery equations and EMR representation in thermal domain- Equivalent thermal model (structural representation) P heat =q s1 T core T surf 13 Heat power source R cond R conv P 2 P 4 C core C surf Equivalent thermal model (EMR representation) C core R cond C surf R conv q s1 Heat Source Tcore T core q s3 q s4 T surf T surf q s6 q s7 Env Electrical parameters are usually fixed - Coupling of electrical and thermal model of battery with EMR- 14 Electrical domain Thermal domain Batt Heat Source q s1 T coren = - R bat P heat = 2 R bat q s1 =P heat /T core R bat is the common element R bat in the coupling element in EMR Electro-thermal coupling R bat EV Traction OCV Storage R bat q s1 T core Thermal EMR 7

8 - Coupling of battery in EMR- Final thermo-electric battery representation Electrical and thermal models coupled with EMR 15 EV Traction OCV storage q s1 T core C core T core q s3 R cond C surf q s4 R conv T surf q s7 T surf Env q s6 With temperature dependent electrical parameters T Core R Bat = R Bat 0 e T 0 C Bat = C Bat 0 + K C T Core Worthy to have this complexity? EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «SIMULATION RESULTS» 8

9 - Validation of the model with temperature dependence- Experimental setup for validation =25 ºC Battery = A123 systems LiFePO4 2,5 Ah 3,3 V 20 C Drive cycle: UAC (c-rate) Temperature (ºC) Simulation Experiment Core Surface With temperature dependence Good dynamic Maximum error in temperature=1,5 ºC 30 Model validated Experimental results from [Lin et al 2013] - Classic and temperature dependent models comparison - Temperature estimation (simulation) =25 ºC Battery modeled = A123 systems LiFePO4 2,5 Ah 3,3 V 20 C Temperature(ºC) Without T dependence With T dependence Core (c-rate) Drive cycle: WLTC Temperature over-estimated Maximum error in temperature= 4 ºC 18 Without temperature dependence Necessity of thermal depence model validated 9

10 - Classic and temperature dependent models comparison - SOC estimation (simulation) =25 ºC Battery modeled = A123 systems LiFePO4 2,5 Ah 3,3 V 20 C SOC (%) Without T dependence With T dependence Drive cycle: WLTC (c-rate) Under-estimated SOC Necessity of thermal dependence model validated 19 Without temperature dependence Maximum error in SOC = 8,4 % - Classic and temperature dependent models comparison - 20 Simulations at different temperatures Battery modeled = A123 systems LiFePO4 2,5 Ah 3,3 V 20 C Drive cycle: WLTC (c-rate) Errors on estimations without temperature dependence Ambient T ( C) 25 C -20 C Max T Core estimation error Max SOC estimation error +4 C -12 C -8.4 % + 10 % 10

11 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «Conclusions» Conclusions and perspectives 22 Battery are the key component of the majority of EV Battery physical principles explains T dependence Temperature dependent on battery electrical parameters is necessary Better estimation of SOC Better estimation of temperature EMR allows easy organization for coupling different physical domains 11

12 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «BIOGRAPHIES AND REFERENCES» - Authors - 24 Angel Nicolas SOLIS University Lille 1, L2EP, France Ing in Electrical Engineering at Univ.National of Río Cuarto (2014) Research topics: EMR, EVs 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 12

13 - Authors - 25 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 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 - References - 26 [Bouscayrol 12] A. Bouscayrol, J. P. Hautier, B. Lemaire-S , "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 [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 [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 [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 , ISSN , [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 , ISSN , [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