On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage-Husa Adaptive EKF

For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO4 battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage-Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process.