Online Parameter Identification for Fractional Order Model of Lithium Ion Battery via Adaptive Genetic Algorithm

Abstract

In order to overcome the shortcomings of the equivalent circuit model and the electrochemical model, a fractional impedance model is established based on the electrochemical impedance spectrum data, and the polarization effect is described in a simple and meaningful way using fractional elements. In this paper, we propose an online parameter identification method for fractional order model (FOM) of lithium ion battery, where an adaptive genetic algorithm is designed to estimation unknown parameters. To this end, an FOM is constructed by using the Grünwald-Letnikov (GL) definition. Then, an unscented kalman filter (UKF) method is adopted to estimate the internal model states. Based on the obtained states, an adaptive genetic algorithm (AGA) is designed to online identify the unknown parameters. Finally, comprehensive experimental verification results are provided to show the effectiveness of the proposed methods.