Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods

Abstract

The accurate determination of electrochemical parameters in lithium-ion batteries is crucial for assessing battery health. This study conducted a comparative investigation utilizing 78 popular meta-heuristic algorithms for parameter identification in simulations. In the electrochemical identification framework proposed herein, the pseudo-two-dimensional model of a lithium-ion battery was solved using the finite element method, and the electrochemical parameters were identified using meta-heuristic algorithms in a one-step strategy. Parameter identification was conducted under high-rate discharge/charge conditions with a loading current of 5C. The discussion encompassed the accuracy, convergence speed, and robustness of the 78 different meta-heuristic algorithms. Notably, the teaching learning-based optimization algorithm exhibited the highest accuracy, albeit with a moderate computational burden. With the exception of the search and rescue optimization algorithm, other algorithms with mean absolute percentage errors of less than 15% demonstrated relatively high robustness. Furthermore, a piecewise C-rates working condition was employed to validate the previous conclusions. Ultimately, this study proposed a modified teaching learning-based optimization algorithm to enhance the precision and computational efficiency of electrochemical parameter identification. This comparative analysis contributed novel insights into electrochemical parameter identification methods employing meta-heuristic algorithms.