Lithium-ion battery, for its promising applications in energy storage systems and electric vehicles, has been increasingly popular. Due to the needs to monitor, predict and control the status of lithium-ion batteries, physical model-based management systems have been intensively studied and developed by many researchers¹. The accuracy and predictability of the model used are of great importance to these systems. In order to link the model to actual battery systems, it is essential to get the parameters needed in the battery models.

Estimating parameters for lithium-ion batteries is challenging due to the complexity of the governing equations, and the possibility of multiple set of parameters that might provide the same accuracy of fitting discharge curves. Parameter estimation of various lithium-ion battery systems has been done for different models, including equivalent circuit model², single-particle model³ and pseudo 2D (P2D) model^4,5. Most of these models are built on known open circuit voltage curves for individual cells, base parameters for cathode/anode thicknesses, porosities, etc.

P2D model, also referred to as Doyle-Fuller-Newman (DFN) model is a first-principle model that has been well studied and experimentally validated. Previous attempts to reduce the computational cost of parameter estimation by incorporate the reformulated P2D model to parameter estimation has been done by V. Ramadesigan⁶. Based on control theory model and parameter identifiability has been studied by researchers which typically concludes that only certain number of parameters can be estimated with good confidence and also identifies operating conditions in which certain parameters will be more sensitive for estimation.

In this presentation, we will show how physics based intuition can help converge on some of the parameters when standard estimation methods fail. We will attempt to address the possibility and relative importance/impact of estimating all the parameters needed for the DFN model from charge discharge curves. Special attention will also be paid for the computation time to enable online state and parameter estimation. This will be facilitated using our past results in model reformulation⁷.

Acknowledgements

The authors are thankful for the financial support of this work by the Clean Energy Institute (CEI) at the University of Washington and the U.S. Department of Energy’s Advanced Research Projects Agency- Energy (ARPA-E).

References

1. V. Ramadesigan et al., J. Electrochem. Soc., 159, R31 (2012).

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

3. A. P. Schmidt, M. Bitzer, Á. W. Imre, and L. Guzzella, J. Power Sources, 195, 5071–5080 (2010)

4. S. Santhanagopalan, Q. Guo, and R. E. White, J. Electrochem. Soc., 154, A198 (2007)

5. J. C. Forman, S. J. Moura, J. L. Stein, and H. K. Fathy, J. Power Sources, 210, 263–275 (2012)

6. V. Ramadesigan et al., J. Electrochem. Soc., 158, A1048 (2011).

7. P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158, A1461 (2011)