Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery

Quan Sun, Hong Zhang, Jianrong Zhang and Wentao Ma
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Quan Sun: School of Microelectronics, Xi’an Jiaotong University, Xi’an 710049, China
Hong Zhang: School of Microelectronics, Xi’an Jiaotong University, Xi’an 710049, China
Jianrong Zhang: Department of Technology, Xi’an Aerosemi Technology Co., Ltd., Xi’an 710077, China
Wentao Ma: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2018, vol. 11, issue 11, 1-20

Abstract: As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises.

Keywords: SOC estimation; UKF; correntropy loss; adaptive; non-Gaussian noises (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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