TY - GEN
T1 - Online estimation of state of charge of Li-ion battery using an iterated extended Kalman particle filter
AU - Zhou, Darning
AU - Ravey, Alexandre
AU - Gao, Fei
AU - Paire, Darien
AU - Miraoui, Abdellatif
AU - Zhang, Ke
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/23
Y1 - 2015/7/23
N2 - 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.
AB - 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.
KW - Battery management system
KW - Extended Kalman filter
KW - Iterated extended Kalman particle filter
KW - Particle filter
KW - State of charge
UR - http://www.scopus.com/inward/record.url?scp=84946125784&partnerID=8YFLogxK
U2 - 10.1109/ITEC.2015.7165762
DO - 10.1109/ITEC.2015.7165762
M3 - 会议稿件
AN - SCOPUS:84946125784
T3 - 2015 IEEE Transportation Electrification Conference and Expo, ITEC 2015
BT - 2015 IEEE Transportation Electrification Conference and Expo, ITEC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Transportation Electrification Conference and Expo, ITEC 2015
Y2 - 14 June 2015 through 17 June 2015
ER -