Model Parameter Identification for Lithium-ion Batteries Based on Fractional Hybrid Genetic-Particle Swarm Optimization Method

A fractional order (FO) circuit model for lithiumion batteries and the corresponding parameter identification method are discussed in this paper. Firstly, a battery fractionalorder model is established, which can accurately characterize the electrochemical processes of lithium-ion battery. Secondly, the fractional Hybrid Genetic-Particle Swarm optimization (fractional HGA-PSO (FHGA-PSO)) method is proposed, parameter identification of the FO model is conducted using the FHGA-PSO and dynamic stress test data. The simulation results validate the effectiveness of the FHGA-PSO with the root-mean-squared error (RMSE) of 11.0 mV and mean relative error (MRE) of 0.2006%, which corresponds to accuracy improvements of 22.54% and 31.30% from the HGA-PSO, respectively.