Estimation of Parameters from Charge-Discharge Curves of Lithium-Ion Batteries Using P2D Model
Authors: Qi Yanbo , Kolluri Suryanarayana , Chen Jerry , Schwartz Daniel T. , Santhanagopalan Shriram , Subramanian Venkat R. 
Published: February 2020

Lithium-ion battery plays a vital role in electric vehicles and energy storage systems. In order to monitor, predict and control the status of lithium-ion batteries, model-based battery management systems (BMS) have been intensively studied and developed by researchers(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 in these models. Recently, Battery Informatics, Inc., a spin-off startup company from the Subramanian group proposed the concept of self-learning BMS. One of the objectives for self-learning BMS is to estimate parameters online as battery operates, where the ability to accurately and quickly estimate parameters that matches charge/discharge curves is essential.

Estimating parameters for the 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 model(2), single particle model(3), and pseudo 2D (P2D) model(4, 5). Most of these models are built on known open circuit voltage curves for individual electrodes, base parameters for cathode/anode thickness, porosities etc.. Recently, Qi et al
(6)estimated open circuit potential of a single electrode together with other parameters using single particle model from a battery discharge curve.

In this presentation, we will show the estimation of electrode open circuit potential together with other parameters of the P2D model. We will attempt to address the possibility and relative importance/impact of estimating all the parameters needed for the P2D model from charge-discharge curves. This will be facilitated using our past results in model reformulation(7)
and parameter estimation for fade analysis (8).

Acknowledgements

The authors 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 #DEAR0000275.

References

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