Parameter identification and state-of-charge prediction of decommissioned lithium batteries

¹ School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui, 241000, China
² Anhui Vocational College Of Defense Techology, Luan, Anhui, 237011, China
³ School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui , 241000, China
⁴ School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui , 241000, China

^a email: 2313605911@qq.com,
^b email: 1094119842@qq.com,
^c email: 409746969@qq.com,
^d* Corresponding author’s e-mail: whzcj_cn@sina.com

Abstract

Aiming at the problem that different temperatures and working modes affect the parameter identification and state of charge (SOC) estimation of decommissioned lithium batteries, a new method based on the second-order RC equivalent circuit model combined with the recursive least square method (RLS) is proposed to introduce the forgetting factor, and combined with the extended Kalman filter algorithm (EKF) to realize the method of online parameter identification of decommissioned lithium batteries and the optimal estimation of SOC. In order to solve the problem of obtaining the optimal solution of the error covariance matrix and the measurement noise covariance matrix in EKF, the particle swarm optimization algorithm (PSO) is used to optimize online to further improve the SOC prediction accuracy. The results show that the joint optimization algorithm can accurately identify the parameters and SOC values of retired lithium batteries, which is helpful to realize the echelon utilization of retired lithium batteries.