High-precision State of Charge Estimation of Lithium-ion Batteries Based on Joint Compression Factor Particle Swarm Optimization-Forgetting Factor Recursive Least Square-Adaptive Extended Kalman Filtering

Abstract

Accurate state of charge (SOC) estimation is an important basis for battery energy management and the applications of lithium-ion batteries. In this paper, an improved compression factor particle swarm optimization-forgetting factor recursive least square (CFPSO -FFRLS) algorithm is proposed, in which the forgetting factor is optimized to identify more accurate parameters for high-precision SOC estimation of lithium-ion battery. In order to improve the SOC estimation accuracy, a dual noise update link is introduced to the traditional extended Kalman filter (EKF), which enhances the algorithm’s ability to adapt to noise by updating the process and measurement noises in real time. The experimental results of parameter identification and SOC estimation show that the CFPSO-FFRLS algorithm proposed significantly improves the accuracy of parameter identification, and the joint CFPSO-FFRLS-AEKF algorithm can accurately estimate the SOC of lithium-ium battery under different working conditions. Under HPPC, BBDST and DST working conditions, the mean absolute errors of SOC estimation are 1.14%, 0.78% and 1.1%, which are improved by 42.71%, 65.79% and 39.56% compared with FFRLS-EKF algorithm, and the root mean square errors are 1.18%, 0.99% and 1.11%, improved by 44.86%, 65.98% and 51.74%, respectively.