«EMR of a battery multi-physical model for electric vehicles»

Transcription

1 EMR Hanoi June 2018 Summer School EMR 18 Energetic Macroscopic Representation «EMR of a battery multi-physical model for electric vehicles» Dr. Ronan GERMAN, Prof. Alain BOUSCAYROL L2EP, Université Lille1, France

2 - Context and objective - 2 Objective: Represent in EMR an electro-thermal model of a Li-ion Battery for EV simulation studies Triple temperature impact on batteries Safety Operation Ageing Very important to include cell temperature in models (simulation studies ) Literature Li-ion LFP Our work 2,5 Ah 3,3 V Small cells [Lin 13] [Forgez 09] 160 Ah 3,3 V Large cells used in EV

3 -Electrification of vehicles- 3 ICE vehicle Energy storage systems (ESS) size Electric vehicle (EV) Volvo S 60 D5 Electrification level km driving range Electrification level Hybrid (HEV) No external recharge µ-hybrid +recovery Full hybrid Plug-in hybrid (PHEV) External recharge Mazda 6 i-eloop Braking energy recovery Stop and start Peugeot 3008 hybrid4 4 km electrical driving range Toyota Prius V «plug in» 23 km electrical driving range

4 -Battery technologies for e-mobility- Market tendencies and forecast 4 Measure Forecast [Pillot 2015] Today 15 %: NiMH 85% : Li-ion NiMH is present in HEVs only NiMH is replaced by Li-ion in HEV Li-ion tends to be the exclusive technology in electromobility in a 5 years horizon This study is focused on Li-ion battery modeling

5 -Summary- 5 Introduction on batteries in electrical vehicles (EVs) Electro-thermal model for one cell Construction of the battery model from the cell model Validation of the battery model

6 EMR Hanoi June 2018 Summer School EMR 18 Energetic Macroscopic Representation «Concepts and definitions»

7 Cell : Battery elementary component - Definitions - 7 Battery capacitance (A.h) 1 A.h means that the battery is fully discharged after 1 h at 1 A State of charge SoC (%) SoC = 0% Battery totally discharged SoC = 100% Battery fully charged Battery energy (kw.h) 1 kw.h =3.6 MJ Plug in Hybrid electric vehicle (PHEV) Electric vehicles (EVs) Golf GTE : 8 kw.h Tazzari Zero: 14.5 kw.h Renault Zoe: 41 kw.h NEDC driving range 50 km 120 km 400 km 1 kwh 8 km NEDC electric driving range

8 Mass Energy (Wh/kg) Comparison of different ESSs - Lithium ion technology in EVs Fuell cell + H2 tank Li-ion Ni-Mh Pb Batteries SCs Capacitors Mass Power (W/kg) In EV the Li-ion battery is the main ESS, Li-ion battery technology Energy density compatible with 300 km autonomy for standard EV Power density compatible with EV acceleration Decreasing price Responsible of Cost Recharge time Driving range of the vehicle Example of 14,5 kwh Li-ion pack placed in thetazzari Zero

9 - Influence factors on Li-ion battery mω SoC= 20% Capacity 2 Ah Parameters instant value 200 mω SoC= 80% SoC= 50% Temperature ( C) ESR 1 Ah [Zhang 17] Battery Capacity influenced by the temperature Battery equivalent series resistance ( ESR) influenced by the SoC and the temperature T 60 C, SoC 100% Ln(C 0 /C) T 60 C, SoC 100% Ln(ESR/ESR 0 ) Ageing Rate T 45 C, SoC 100% T 45 C, SoC 100% temperature increases ageing rate SoC insreases ageing rate T 45 C, SoC 65% T 45 C, SoC 65% Time (h) [Baghdadi 16] Include temperature and SoC in battery model for EV simulation Time (h)

10 - Li-ion battery in studied EV- 10 Battery elementary component : 1 Cell 65 mm Cells are placed side by side in a module 183 mm C Cell Nom : 160 Ah U Cell : 4V->2.5V Mass : 5.68 kg 275 mm Modules are placed in the EV for mass repartition Rear Modules are connected together to achieve high battery voltage u Bat =24.u Cell Module 1 Module Front Module 2 Module1 =7 Cells i Bat Module 2 =10 Cells Module3 =7 Cells

11 EMR Hanoi June 2018 Summer School EMR 18 Energetic Macroscopic Representation «Electro-thermal model for one cell»

12 Real system -Energetic Macroscopic Representation [Bouscayrol 12]- Subsystems dynamical Models + Controllers Unified representation systemic organization EMR 12 Simulation studies Energetic Macroscopic Representation (EMR) Causality principle: Output delayed compared to input 4 basic pictograms (In x Out=Power) Source Battery Bat. Mono Phys. Accumulation Multi Phys. Source Conv. Conv. Chopper DCM Winding E/M conv Mechanical part U Bat U DCM I DCM T DCM i Bat I DCM FEM DCM Ω DCM m MS Example of the torque control of a DC Machine (T DCM ) m Ref FEM DCM Mes I DCM Mes U bat Mes T DCM Ref I DCM Ref T DCM Ref Control structure systematically deduced by mirror effect

13 OCV (SoC,T) Structural representation Energy losses (connectors, electrodes, electrolyte ) Conversion Electrochemical storage Voltage source - Electrical model - R S (SoC, T) u i Cdl i Rt C dl (SoC,T) R t (SoC,T) u RC u Cell Traction system Current source 13 Voltage coupling Charge transfer and diffusion Current coupling Accumulation Conversion Energetic Macroscopic Representation (EMR [Bou 12]) OCV OCV R S Voltage coupling u u Cell Tract. R t u RC i Cdl Cdl i Rt u RC u RC Current coupling

14 -Introduction to cell thermal modeling- 14 Important notions Thermal capacitance (J/K) Thermal energy storage Hypothesis Heat source at the core center Conduction only in solid Convection only for solid to gas heat transfer Thermal resistances are located at the interfaces Thermal capacitance of the package neglected P heat = Thermal resistance (K/W) Resistance to the power transfert 1cell - Package surface Air T amb + Core Air T amb Equivalent circuit thermal model R S. ²+R t.i Rt ² T core P core P Out R cond T surf R conv T amb T amb C core

15 Structural representation For thermal domain [Hor 16] q S : entropy flow (W/K) T: Temperature (K) -EMR for thermal model- P heat = R S. ²+R t.i Rt ² = q Stot. T core T core P core = q S2. T core C core P Out = P Out = q S3. T core q S5. T amb R cond +R conv T amb 15 T amb q S1 EMR T core R S T core q S1 R t C core T Core q Stot Rcond + R conv T core q S5 Air q S3 T Amb

16 -Coupling thermal and electrical domains by EMR- 16 Resistances are multi-physical ( electro-thermal) conversion elements Resistances are at the border between thermal and electrical domains EMR of the electro-thermal model R Electrical domain S OCV u OCV Voltage coupling u Cell Thermal domain T core q S1 T core R t i Rt u RC i Cdl C dl q Stot q S1 u RC u RC Current coupling T Core C core R cond + R conv T core q S3 q S5 T Amb Air

17 EMR Hanoi June 2018 Summer School EMR 18 Energetic Macroscopic Representation «From the cell to the battery»

18 Assumptions -Li-ion battery in studied EV- Cells are not thermally influenced by surrounding cells Cells are identical Use adaptation elements 18 = i Mod1 i Mod1 = i Pack u Cell. 7 = u Mod1 u Mod = u Cell Electrical domain OCV OCV R S u 1 cell EMR Voltage coupling Module1 EMR Battery EMR Adaptation 1 Adaptation 2 u Cell u Mod1 u Bat i Bat i Mod1 i Bat T core q S1 R t u RC i Rt i Cdl C dl Thermal domain T core q S1 q Stot u RC u RC Current coupling T Core C core R cond + R conv T core q S3 q S5 T Amb Air

19 -Experimental protocol for pack model validation- 19 Instrumenting a module in the studied EV Choosing a varied road Campus (urban) Driving imod1 Module1 T CoreCell + TAmb - TAmb Road (sub-urban) umod1 V TAmbMod1 N Compare model and experimental results Experimental umod1 (V) Simulation Time time (s) Created at gpsvisualizer.com with google maps TCoreCell ( C) ΔTMax Time time (s) (s) Relative absolute error on voltage : 7 % Relative absolute error on temperature : 4.8 % Battery model is validated with a real driving cycle in a real EV (ε<10%)

20 -Conclusion- 20 EMR organization and coupling of classical thermal and electrical cell models Assumptions have been made to build the battery electro-thermal model from the cell model Onboard validation a with an instrumented EV module during driving

21 - Authors - 21 Dr. Ronan German University Lille 1, L2EP, France PhD in Electrical Engineering at Univ. Lyon 1 (2013) Research topics: Battery Modelling, Energy management of multi-sources vehicles Prof. Alain BOUSCAYROL University 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

22 EMR Hanoi June 2018 Summer School EMR 18 Energetic Macroscopic Representation «BIOGRAPHIES AND REFERENCES»

23 - References - 23 [Baghdadi 16] I. Baghdadi, O. Briat, J.-Y. Delétage, P. Gyan, et J.-M. Vinassa, «Lithium battery aging model based on Dakin s degradation approach», Journal of Power Sources, vol. 325, p , sept [Bouscayrol 12] A. Bouscayrol, J.-P. Hautier, et B. Lemaire-S , Systemic design methodologies for electrical energy systems-chapter 3: Graphic formalism for the control of multi-physical energetic systems: COG and EMR, Wiley. New York, NY, USA, [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 , [German 17] R. German, S. Shili, A. Sari, P. Venet, et A. Bouscayrol, «Characterization Method for Electrothermal Model of Li-Ion Large Cells», in 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), 2017, p. 1-6 [German 18] R. German, P. Delarue, et A. Bouscayrol, «Battery pack self-heating during the charging process», in 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, p [Horrein 16] L. Horrein, A. Bouscayrol, W. Lhomme, et C. Depature, «Impact of heating system on the range of an electric vehicle», IEEE Transactions on Vehicular Technology, 2016.

24 -References- 24 [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 [Pillot 2015] C. Pillot, «Battery Market Development for Consumer Electronics, Automotive, and Industrial: Materials Requirements and Trends», Avicenne Energy, [Redondo 16] E. Redondo-Iglesias, P. Venet, and S. Pelissier, Measuring Reversible and Irreversible Capacity Losses on Lithium-Ion Batteries, presented at the Vehicle Power and Propulsion Conference (VPPC), pp. 1 5, 2016 IEEE, [Yi 13] J. Yi, U. S. Kim, C. B. Shin, T. Han, et S. Park, «Modeling the temperature dependence of the discharge behavior of a lithium-ion battery in low environmental temperature», Journal of Power Sources, vol. 244, p , déc [Zhang 17] Y. C. Zhang, O. Briat, J. Y. Deletage, C. Martin, G. Gager, et J. M. Vinassa, «Performance quantification of latest generation Li-ion batteries in wide temperature range», in IECON rd Annual Conference of the IEEE Industrial Electronics Society, 2017, p