A SOC Estimation Method for Energy Storage Batteries Based on FFRLS-EKF

To address the issues of significant cumulative errors and the inability to track in real time in traditional state of charge (SOC) estimation methods for energy storage batteries, this paper proposes an SOC estimation method based on the Forgetting Factor Recursive Least Squares-Extended Kalman Filter (FFRLS-EKF). First, create an equivalent circuit model for the energy storage battery Thevenin, and then use the Forgetting Factor Recursive Least Squares (FFRLS) method to identify the model parameters online based on past operating data. Furthermore, based on the Extended Kalman Filter (EKF) method, a SOC estimate model for energy storage batteries is created based on identifying characteristics and previous operating data. Finally, MATLAB was used to simulate and verify the online identification of Thevenin equivalent circuit model parameters, as well as the calculation of energy storage battery state of charge. The results demonstrated that the method described in this research may properly and reliably assess SOC of energy storage batteries.