On-line parameter identification and SOC estimation of nonlinear model of lithium-ion battery based on Wiener structure

Lithium-ion battery is a nonlinear electrochemical system, accurate battery model is the premise of studying the characteristics of lithium-ion batteries. This paper mainly studies the parameter identification and state of charge (SOC) estimation of nonlinear model of lithium-ion battery based on Wiener structure. Firstly, the auxiliary model modified forgetting gradient (AMMFG) algorithm is used to identify the parameters, and then the terminal voltage of lithium-ion battery is predicted according to the identified parameters. Secondly, the H-infinity filtering (HIF) algorithm is used to estimate SOC on-line. The interactive estimation based on the above two algorithms realizes the on-line identification and SOC estimation of lithium-ion battery parameters. In addition, the AMMFG-HIF joint estimation algorithm is verified under dynamic stress test (DST), Federal Urban Driving Schedule (FUDS) and different ambient temperature conditions. The results show that both the established model and the AMMFG-HIF algorithm have high accuracy, and the SOC estimation error can be kept at a low level. Finally, the robustness analysis of the AMMFG-HIF algorithm is completed, and the algorithm shows strong robustness under disturbances.