Efficiency and Optimality in Electrochemical Battery Model Parameter Identification: A Comparative Study of Estimation Techniques

TL;DR

This study compares three parameter identification methods for electrochemical battery models, finding PSO to be the most accurate and stable, LS suitable for minor adjustments, and GA less efficient overall.

Contribution

It provides a comprehensive comparison of LS, PSO, and GA methods for battery model parameter identification, highlighting PSO's superior performance.

Findings

PSO outperforms in accuracy and stability

LS is effective for minor parameter adjustments

GA is less efficient and less accurate

Abstract

Parameter identification for electrochemical battery models has always been challenging due to the multitude of parameters involved, most of which cannot be directly measured. This paper evaluates the efficiency and optimality of three widely-used parameter identification methods for electrochemical battery models: Least Squares Method (LS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Therefore, a Single Particle Model (SPM) of a battery was developed and discretized. Battery parameter grouping was then performed to reduce the number of parameters required. Using a set of parameters previously identified from a real battery as a benchmark, we generated fitting and validation datasets to assess the methods' runtime and accuracy. The comparative analysis reveals that PSO outperforms the other methods in terms of accuracy and stability, making it highly effective for parameter identification when there is no prior knowledge of the battery's internal parameters. In contrast, LS is better suited for minor adjustments in parameters, particularly for aging batteries, whereas GA lags behind in both computational efficiency and optimality with respect to PSO.