Adaptive parameter identification method and state of charge estimation of Lithium Ion battery

摘要

Lithium ion (li-ion) battery state of charge (SOC) estimation is a key function of battery management system and critical for the reliable and secure operations of batteries. Based on the RC equivalent circuit model (ECM) of li-ion battery, variable forgetting factor recursive least square (VFFRLS) adopted as an adaptive parameter identification method is suited to the nonlinear and time varying parameter battery model identification. Extended Kalman filter (EKF) technique is often used as the SOC estimation algorithm, in order to improve the estimation accuracy, an alternative nonlinear Kalman filter technique known as cubature Kalman filter (CKF) is then employed. The experimental results show that the CKF algorithm outperforms EKF in the li-ion battery estimation application with the maximum error being less than 2.3%.