Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Caiping Zhang, Jiuchun Jiang, Weige Zhang and Suleiman M. Sharkh
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Caiping Zhang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Jiuchun Jiang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Weige Zhang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Suleiman M. Sharkh: School of Engineering Sciences, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Energies, 2012, vol. 5, issue 4, 1-18

Abstract: A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.

Keywords: lithium-ion batteries; SOC estimation; robust estimation; EKF; HEV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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