State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

Jiandong Duan, Peng Wang, Wentao Ma, Xinyu Qiu, Xuan Tian and Shuai Fang
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Jiandong Duan: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Peng Wang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Wentao Ma: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Xinyu Qiu: NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
Xuan Tian: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Shuai Fang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2020, vol. 13, issue 16, 1-18

Abstract: State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

Keywords: SOC estimation; extended Kalman filter; maximum correntropy criterion; weighted least squares; non-Gaussian noise (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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