A SOC Estimation Method for Internal Short Circuit Battery based on EKF-FFRLS Algorithm

Lithium-ion batteries are usually connected in series in large-scale battery energy storage systems (BESSs), and the safety operation of battery packs has become a hotspot. The accurate estimation of the State of Charge (SOC) is a key factor in ensuring the safe and reliable operation of battery packs. The SOC of the battery is important for the diagnosis of internal short circuit (ISC) fault, while the external characteristics of the ISC fault are not obvious, and the continuous micro short circuit discharge of the battery brings difficulties to the accurate estimation of the SOC. This paper proposes a SOC estimation method for ISC battery, which combines Extended Kalman Filtering (EKF) and Recursive Least Squares with Forgetting Factor (FFRLS). An equivalent circuit model of the ISC battery is established first and the FFRLS algorithm is used to identify model parameters. Then, the EKF algorithm and the model parameters obtained through identification are employed to estimate SOC. The experimental results demonstrate that the proposed method can effectively estimate the battery SOC under the FUDS discharging condition and the maximum estimation error of SOC is 0.9%.