An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error

Deng Ma, Kai Gao, Yutao Mu, Ziqi Wei and Ronghua Du
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Deng Ma: International College of Engineering, Changsha University of Science & Technology, Changsha 410114, China
Kai Gao: College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China
Yutao Mu: International College of Engineering, Changsha University of Science & Technology, Changsha 410114, China
Ziqi Wei: International College of Engineering, Changsha University of Science & Technology, Changsha 410114, China
Ronghua Du: College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: Accurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied. However, EKF cannot achieve valid estimation when the model accuracy is inadequate, the noise covariance matrix is uncertain, and the sensor has large errors. This paper makes two contributions to overcome these drawbacks: (1) A variable forgetting factor recursive least squares (VFFRLS) is proposed to accomplish parameters identification. This method updates the forgetting factor according to the innovation sequence, which accuracy is superior to the forgetting factor recursive least squares (FFRLS); (2) an adaptive tracking EKF (ATEKF) is proposed to estimate the SOC of the battery. In ATEKF, the error covariance matrix is adaptively corrected according to the innovation sequence and correction factor. The value of the correction factor is related to the actual error. Proposed algorithms are validated with a publicly available dataset from the University of Maryland. The experimental results indicate that the identification error of VFFRLS can be reduced from 0.05% to 0.018%. Additionally, ATEKF has better accuracy and robustness than EKF when having large sensor errors and uncertainty of the error covariance matrix, in which case it can reduce SOC estimation error from 1.09% to 0.15%.

Keywords: SOC; EKF; model uncertainty; sensor error (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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