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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Quan Sun & Hong Zhang & Jianrong Zhang & Wentao Ma, 2018. "Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 11(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3123-:d:182246
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    References listed on IDEAS

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    1. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
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    4. Xi Liu & Badong Chen & Bin Xu & Zongze Wu & Paul Honeine, 2017. "Maximum correntropy unscented filter," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(8), pages 1607-1615, June.
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    Citations

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    Cited by:

    1. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    2. Miaomiao Zeng & Peng Zhang & Yang Yang & Changjun Xie & Ying Shi, 2019. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm," Energies, MDPI, vol. 12(16), pages 1-15, August.
    3. Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    4. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    5. Liu, Zheng & Zhao, Zhenhua & Qiu, Yuan & Jing, Benqin & Yang, Chunshan & Wu, Huifeng, 2023. "Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction," Energy, Elsevier, vol. 283(C).

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