A Novel Fitting Method of Electrochemical Impedance Spectroscopy for Lithium-Ion Batteries Based on Random Mutation Differential Evolution Algorithm

Electrochemical impedance spectroscopy (EIS) is widely used to diagnose the state of health (SOH) of lithium-ion batteries. One of the essential steps for the diagnosis is to analyze EIS with an equivalent circuit model (ECM) to understand the changes of the internal physical and chemical processes. Due to numerous equivalent circuit elements in the ECM, existing parameter identification methods often fail to meet the requirements in terms of identification accuracy or convergence speed. Therefore, this article proposes a novel impedance model parameter identification method based on the random mutation differential evolution (RMDE) algorithm. Compared with methods such as nonlinear least squares, it does not depend on the initial values of the parameters. The method is compared with chaos particle swarm optimization (CPSO) algorithm and genetic algorithm (GA), showing advantages in many aspects. The method has a convergence speed much faster than CPSO; the fitting accuracy of RMDE is more than 10 times that of CPSO and GA; the consistency of the parameter identification results of RMDE is better than the other algorithms. It is expected to complete the EIS fitting in a powerful local computing unit or cloud server, thereby facilitating the battery SOH diagnosis.