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C BD Battery Design LLC Advanced Thermal Modeling of Batteries Empirical Battery Models Empirical Battery Models STAR Asia Conference December 2013 Overview Battery Design Process Use of Physics-based model for synthetic data

  1. C BD Battery Design LLC Advanced Thermal Modeling of Batteries Empirical Battery Models Empirical Battery Models STAR Asia Conference December 2013

  2. Overview  Battery Design Process  Use of Physics-based model for synthetic data generation  Parameters for RCR Table Model  Evaluation of RCR Table Model Fits  Conclusion 2

  3. Overview of Battery Design Process with Empirical Model Use model to Test cell Fit data to model simulate battery performance What What model? Can battery deliver procedure(s) to required use? How to validate performance? fit? What Is heat ‐ transfer temperatures? adequate to ensure max. temperature not exceeded and uniform? 3

  4. Selected Simulation Models in Battery Design Studio Consumer HEV/PHEV Electronics, Cell Design Module/Pack EV NTG DISTNP RCR (1) J. Newman and W. Tiedemann, J. M. Verbrugge and R. Conell, J. (1) T. Fuller, M. Doyle, J. Newman, J. Electrochem. Soc. Vol. 140, No. 7, July 1993 pp. Electrochem. Soc . 149 (2002) A45-A53 Electrochem. Soc . 141 (1994) 1-10 1961-1968. (2) Battery Design LLC, “BDS (2) H. Gu, J. Electrochem. Soc. , Vol. 130 No. 7 Documentation” 1983 pp. 1459-1464. (3) U. S. Kim, Ch.B. Shin, C.-S. Kim, J. Power Src. 189 (2009) 841-846 Simple, easy to create Useful for design. Quick response for model, best for simple Solves transport, frequent charge/ discharge/charge, kinetics, equations discharge like HEV/PHEV Page 4 Page 4 thermal analysis 4

  5. Battery Physics – DISTNP Model DISTNP model allows prediction of cell performance based on materials properties and cell design. T. Fuller, M. Doyle, J. Newman , J. Electrochem. Soc ., Vol. 141 (1994) 1 ‐ 10 5

  6. DISTNP model can simulate aging 2C rate Impedance rise and capacity loss due to SEI growth. 4C rate 6

  7. Lithium ‐ Ion Battery Transport Solid ‐ and Liquid ‐ Phase Gradients L   1      aj E dx loc S L surf Solid Diffusion Polarization j tot 0 L   Kinetic Overpotential 1   aj E E dx loc surf ave j tot 0 L 2 Liquid-Phase Ohmic drop 1 j  L dx  j tot eff 0 Solid-Phase Ohmic drop L 2 1 j  S dx  j Electrolyte diffusion polarization tot eff 0  L RT c 1 2    L j dx  c L j c F x tot L 0  j appl R Contact Resistance Losses contact A. Nyman, T. G. Zavalis, R. Elger, M. Behm, G. Lindbergh “Analysis of the Polarization in a Li ‐ Ion Battery Cell by Numerical Simulation” J. Electrochem. Soc. , 157 (11) A136 ‐ A1246 (2010). DISTNP model allows sources of voltage loss in cell to be quantitatively assigned to specific physical processes. 7

  8. Approach to parameterize RCR model in BDS Simulate HPPC test using RCR Generate Compare to Use BDS gap model synthetic synthetic tool to data with data parameterize DISTNP RCR model Simulate Drive model Cycle using RCR model Questions: • For HPPC test, how sensitive are fitted parameters/simulation results to: • pulse duration? 2 s, 10 s, 30 s • pulse currents? 5 C rate versus 10 C rate • Temperature? 10, 20, 40, 50  C • How well does temperature interpolation work? 8

  9. DISTNP Model Cell Used for Generation of Synthetic Data Model uses two different particle sizes, realistic electrolyte properties, temperature dependent solid-phase diffusion and kinetics.

  10. Generate Synthetic Data Testing should be done under Goal should be to emulate controlled temperature and ideally good physical testing. cell temperature will be uniform.   Thin, pouch cell is ideal for characterizing electrochemical behavior. Cell can be clamped with aluminum plates and placed vertically in environmental chamber to provide well- defined boundary conditions for heat transfer between cell and chamber. 10

  11. Generate Synthetic Data (cont.) http://www.uscar.org/guest/teams/11/U-S-DRIVE- Electrochemical-Energy-Storage-Tech-Team Hybrid Pulse Power Characterization (HPPC) Test provides convenient, standardized method to characterize impedance and voltage relaxation of battery of state of charge from 90 to 10%. 11

  12. Analysis of Voltage Losses in HPPC Test Contact resistance, a purely ohmic loss, is major source of voltage loss. At 10  C the voltage loss due to electrolyte diffusion polarization and activation overpotential are much larger than at 30  C. At 30  the voltage loss is mainly due to contact resistance and electrolyte diffusion polarization. 12

  13. Breakdow n of voltage losses in HPPC Discharge Pulse Major source of voltage drop is contact resistance between electrode coating and current collector 13

  14. RCR Table Model Parameters can be entered as tables. Interpolation between values as a function of state of charge is done using Bezier splines. Linear interpolation is used to compute values at intermediate temperatures. Model is easy to use and computationally efficient. 14

  15. Gap tool in BDS provides automatic regression of USABC Equivalent Circuit Model (RCR type) User simply selects data file with HPPC data and Gap tool automatically generates table of RCR parameters. 15

  16. RCR Parameters can be entered into BDS RCRTable Model One table for each temperature can be entered. Linear interpolation is used to obtain parameter values at intermediate temperatures. 16

  17. 17 Entered parameters for 10, 20, 40 and 50 °C Parameterized RCRTable Model

  18. Drive Cycle Simulation: US06 PHEV Charge Depleting A Ah 18

  19. Comparison of computation times 45.9 min RCRTable model is ~ 10 4 times faster than DIST 0.2 s model 19

  20. COMPARISON OF RCR MODEL TO SYNTHETIC DATA 20

  21. Comparison of Fitted HPPC test to synthetic data RMS error = 17 mV 10 °C, 5 C rate, 10 s pulse 21

  22. 22 RMS error = 12 mV Drive Cycle Simulation at 10  C: Voltage 10°C, 5 C rate, 10 s pulse

  23. Drive Cycle Simulation at 10  C: Temperature 10°C, 5 C rate, 10 s pulse RMS error = 0.08 °C 18 16 14 12 10 23

  24. Drive Cycle Simulation at 30  C: Voltage Parameters Interpolated from 20 and 40  C 30°C, 5 C rate, 10 s pulse RMS error = 12.8 mV 24

  25. Drive Cycle Simulation at 30  C: Temperature Parameters Interpolated from 20 and 40  C RMS error = 0.108 °C 30°C, 5 C rate, 10 s pulse 36 34 32 As time progresses 30 differences in temperature accumulate. 25

  26. Summary of Drive Cycle Simulations 55  31.6 % SOC 5C Rate - RMS Error 5C Rate - RMS Error milliVolts degrees C Pulse Time, s Pulse Time, s 2 10 30 2 10 30 T, °C T, °C 10 7.7 12.0 10 0.044 0.012 20 13.8 13.9 13.0 20 0.163 0.149 0.063 30 13.2 12.8 17.3 30 0.136 0.108 0.253 40 8.7 13.1 13.3 40 0.202 0.155 0.153 average 10.8 13.0 14.5 average 0.14 0.11 0.16 * Interpolated Comments • Predictions are very good, 2 s give best overall voltage simulation • Fits at 10C rate were comparable 26

  27. Drive Cycle Simulation at 20  C: Voltage RMS error = 33 mV 20°C, 5 C rate, 10 s pulse 70  23.4% SOC 27

  28. Drive Cycle Simulation at 20  C : Temperature 70  23.4% SOC 10°C, 5 C rate, 10 s pulse RMS error = 0.64 °C 35 30 25 ~ 15°C temperature rise 20 28

  29. Summary  Physics-based model is useful for generating synthetic data • Realistic simulations can account for different particle sizes, electrode formulations, separators, electrolytes, kinetics, aging  RCR Table model is computationally efficient and provides excellent prediction for data sets where voltage losses are mainly ohmic. • Predicted voltage and temperature correspond closely for parameters obtained over range of rates and temperatures 29

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