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Article

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

1
School of Microelectronics, Xi’an Jiaotong University, Xi’an 710049, China
2
Department of Technology, Xi’an Aerosemi Technology Co., Ltd., Xi’an 710077, China
3
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
*
Authors to whom correspondence should be addressed.
Energies 2018, 11(11), 3123; https://doi.org/10.3390/en11113123
Received: 6 October 2018 / Revised: 1 November 2018 / Accepted: 10 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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. View Full-Text
Keywords: SOC estimation; UKF; correntropy loss; adaptive; non-Gaussian noises SOC estimation; UKF; correntropy loss; adaptive; non-Gaussian noises
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MDPI and ACS Style

Sun, Q.; Zhang, H.; Zhang, J.; Ma, W. Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery. Energies 2018, 11, 3123. https://doi.org/10.3390/en11113123

AMA Style

Sun Q, Zhang H, Zhang J, Ma W. Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery. Energies. 2018; 11(11):3123. https://doi.org/10.3390/en11113123

Chicago/Turabian Style

Sun, Quan; Zhang, Hong; Zhang, Jianrong; Ma, Wentao. 2018. "Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery" Energies 11, no. 11: 3123. https://doi.org/10.3390/en11113123

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