Parameter Estimation of Lithium-Ion Battery Models Using a Novel Tanks-in-Series Approach
Authors: Kolluri Suryanarayana , Subramaniam Akshay , Mittal Prateek , Preger Yuliya , Shah Krishna , Subramanian Venkat R. 
Published: December 2020

Lithium-ion batteries play a vital role in electric vehicles and energy storage systems. In order to monitor, predict and control the performance of lithium-ion batteries, advanced Battery Management Systems (BMS) are key. Intensive efforts are being pursued to develop electrochemical model-based battery management systems (BMS).1 The accuracy and predictability of a model depends on the precision of its parameters. Precise parameter estimation is critical for efficient BMS performance. Moreover, model parameters tend to change with battery aging. Efficient BMS performance requires the accurate tracking and update of relevant parameters.
Estimating parameters for electrochemical models is challenging due to the complexity of the governing equations, and the possibility of degeneracy, with multiple sets of parameter values that give the same accuracy for fitting charge/discharge data. Parameter estimation of various lithium-ion battery systems has been attempted with different model types, including equivalent circuit model2, single particle model3 and pseudo 2D (P2D) model.4,5 Literature approaches to estimate parameters using GA6, heuristic algorithms7 etc., are difficult to implement in real-time due to computational complexity. In the past, we proposed and implemented reformulated p2D models for real-time parameter estimation.8,9 While the reformulated models are computationally inexpensive, the large set of parameters and resulting likelihood of degeneracy introduce substantial complexity.
In this presentation, we propose to estimate parameters of the recently published Tanks-in-Series model10. This model is generated by systematic volume-averaging of the p2D model that enables efficient and real-time simulation. The number of parameters to be estimated in the tank model can be reduced by grouping the transport, design and kinetic parameters. The estimation is performed on experimental charge/discharge data for cylindrical cells. The accuracy of the estimated parameters is validated by using a dynamic charging profile (FUDS cycle test) and measuring the error in voltage-time curves.

Acknowledgements

The authors acknowledge funding from the Department of Energy, Office of Electricity, Energy Storage Program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 and the Texas Materials Institute at The University of Texas at Austin.

References

V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 159, R31–R45 (2012).

Y. Hu, S. Yurkovich, Y. Guezennec, and B. J. Yurkovich, Control Eng. Pract., 17, 1190–1201 (2009).

A. P. Schmidt, M. Bitzer, Á. W. Imre, and L. Guzzella, IFAC Proc. Vol., 43, 198–203 (2010).

J. C. Forman, S. J. Moura, J. L. Stein, and H. K. Fathy, in 5th Annual Dynamic Systems and Control Conference,, p. 1–8 (2012).

S. Santhanagopalan, Q. Guo, and R. E. White, J. Electrochem. Soc., 154, 198–206 (2007).

A. Jokar, B. Rajabloo, M. Désilets, and M. Lacroix, J. Electrochem. Soc., 163, A2876–A2886 (2016).

J. Li, L. Zou, F. Tian, X. Dong, Z. Zou, and H. Yang, J. Electrochem. Soc., 163, A1646–A1652 (2016).

V. Boovaragavan, S. Harinipriya, and V. R. Subramanian, J. Power Sources, 183, 361–365 (2008).

V. Ramadesigan, K. Chen, N. A. Burns, V. Boovaragavan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 158, A1048–A1054 (2011).

A. Subramaniam, S. Kolluri, C. D. Parke, M. Pathak, S. Santhanagopalan, and V. R. Subramanian, J. Electrochem. Soc., 167, 013534-013534–18 (2020).