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, intensive efforts by various researchers are being pursued to develop an electrochemical model-based battery management systems (BMS)¹. The accuracy and predictability of the model used are of great importance to these systems, which heavily depends on the precision of the parameters needed for these models. Estimation of the model parameters is critical for the BMS to perform efficiently. Moreover, the parameters of a battery might change as the battery ages. Tracking those parameters is required to maximize the efficiency of the BMS. In addition to this, the parameters of the battery need to be estimated online.

Estimating parameters for the lithium-ion batteries is challenging due to the complexity in solving the governing equations, and the possibility of degeneracy, with multiple sets of parameter values that might provide the same accuracy for fitting charge/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. However, most of these models are built on known open circuit voltage curves for individual electrodes, along with geometric and transport base parameters for cathode/anode thicknesses, porosities etc.

In this presentation, we propose a method to estimate various parameters of the P2D model, along with the open circuit potential of the cathode. The parameters are estimated using the experimental charge/discharge curves for two different types of cell formats. The accuracy of the parameters is validated by implementing a dynamic charging profile, and measuring the error in the voltage-time curves experimentally. We also attempt to address the possibility and relative importance/impact of estimating all the parameters needed for the P2D model using just charge/discharge curves. This will be facilitated using our past results in fast simulation of battery models^6,7.

Acknowledgements

The authors would like to thank the United States Department of Energy (DOE) for the financial support for this work through the Advanced Research Projects Agency – Energy (ARPA-E) award #DE-AR0000275.

References

1. V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz and V. R. Subramanian, J. Electrochem. Soc., 159, R31 (2012).
2. Y. Hu, S. Yurkovich, Y. Guezennec and B. J. Yurkovich, Control Eng. Pract., 17, 1190 (2009).
3. A. P. Schmidt, M. Bitzer, Á. W. Imre and L. Guzzella, J. Power Sources, 195, 5071 (2010).
4. J. C. Forman, S. J. Moura, J. L. Stein and H. K. Fathy, J. Power Sources, 210, 263 (2012).
5. S. Santhanagopalan, Q. Guo and R. E. White, J. Electrochem. Soc., 154, A198 (2007).
6. V. R. Subramanian, V. Boovaragavan, and V. D. Diwakar, Electrochem. Solid St., 10, A255 (2007).
7. M. T. Lawder, V. Ramadesigan, B. Suthar and V. R. Subramanian, Computers & Chemical Engineering, 82, 283 (2015).