Online estimation of state of charge of Li-ion battery using an iterated extended Kalman particle filter

Battery state of charge (SOC) estimation is a key issue in battery management system (BMS) for ensuring reliable operation of electric vehicles (EV). This paper proposes a novel SOC estimation method based on iterated extended Kalman particle filter (IEKPF), the main characteristics of IEKPF are to generate the proposal distribution, an accurate approximation of the posterior probability density can then be achieved, the resulting a better candidate can be used for proposal distributions in particle filter framework. Two experiments are carried out to evaluate the performance of the presented method. The results show that IEKPF can achieve higher accuracy of SOC estimation than using traditional algorithms particle filter (PF) and extended Kalman filter (EKF). Besides, the proposed method has a better performance in the longer discharge phase experiment.