A study on SOC prediction enhancement using STEKF in lithium batteries

Abstract Accurate State of Charge (SOC) estimation for lithium batteries is essential for optimizing battery performance and lifespan. This research proposes a SOC prediction technique utilizing the Strong Tracking Extended Kalman Filter (STEKF) to overcome the drawbacks of the conventional Extended Kalman Filter (EKF) in highly dynamic and uncertain scenarios. By dynamically modifying the error covariance matrix, STEKF greatly improves the adaptability and precision of the prediction model. Experimental findings indicate that STEKF decreases the Root Mean Square Error (RMSE) from 4.7% to 1.9% at 25°C, surpassing the traditional EKF and providing a more accurate and reliable SOC prediction method for electric vehicle battery management systems (BMS).