Symbolic Regression for State Estimation of Lithium-Ion Battery

Abstract

Modeling lithium-ion batteries has been a challenging problem. One of the critical tasks among many is state estimation, as it enables researchers to design better battery management systems (BMS). Understanding important battery parameters allows researchers to monitor battery health, predict performance, and optimize battery operation. Traditionally, mathematical models using partial differential equations (PDEs) such as the pseudo two-dimensional model (P2D) have been widely used to estimate physical quantities within the battery. However, deployment of P2D for real-time prediction is limited by the high computational cost, instability of numerical techniques, and the requirement of specialized software. Recent studies have successfully applied various machine learning algorithms achieving high predictive accuracy in many cases. These algorithms, however, suffer from limitations on generalizability and high computation requirements, which limit their deployment. We investigate the applicability of symbolic regression(SR), a branch of symbolic AI techniques to the problem. The results demonstrate equivalent accuracy with P2D while offering orders of magnitude faster execution. As this study uses simulated P2D data, the findings should be interpreted as a proof-of-concept indicating that symbolic regression can yield interpretable, computationally efficient surrogates with promising BMS relevance.