TY - GEN
T1 - State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)
AU - Sangwan, Venu
AU - Kumar, Rajesh
AU - Rathore, Akshay Kumar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/8
Y1 - 2017/11/8
N2 - A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.
AB - A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.
KW - Battery electric vehicle
KW - Battery management system
KW - Central difference Kalman filter
KW - Extended Kalman Filter
KW - Li-ion batteries
KW - State of charge estimation
UR - http://www.scopus.com/inward/record.url?scp=85044101297&partnerID=8YFLogxK
U2 - 10.1109/IAS.2017.8101722
DO - 10.1109/IAS.2017.8101722
M3 - Conference contribution
AN - SCOPUS:85044101297
T3 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
SP - 1
EP - 6
BT - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
Y2 - 1 October 2017 through 5 October 2017
ER -
