FFRLS-AEKF Based SOC Estimation Approach for Li-ion Battery Applied in Electric Boat System

In recent years, the study in SOC (state of charge) estimation of lithium-ion batteries has become the focus in the field of new energy. An accurate estimation of SOC can prevent battery from overcharging and over discharging for its safety. This paper introduces an innovation-based adaptive Extended Kalman Filter (EKF) algorithm with the Recursive Least Squares with Forgetting Factor (FFRLS) identification method to precisely predict the SOC value of a single LFP battery. In an experiment using the real data of electric boat system, the estimation error is lower than 3%. Moreover, a balance strategy is employed to estimate a total SOC of a battery cluster. This new balance strategy can help us enhance the accuracy of assessing total remaining power.