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State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

Author

Listed:
  • Xiaopeng Tang

    (Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong, China)

  • Boyang Liu

    (Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong, China)

  • Furong Gao

    (Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong, China
    Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China)

  • Zhou Lv

    (Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China)

Abstract

A battery’s state-of-charge ( SOC ) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.

Suggested Citation

  • Xiaopeng Tang & Boyang Liu & Furong Gao & Zhou Lv, 2016. "State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer," Energies, MDPI, vol. 9(9), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:675-:d:76595
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    References listed on IDEAS

    as
    1. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
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    Cited by:

    1. Agarwal, Daksh & Potnuru, Rakesh & Kaushik, Chiranjeev & Darla, Vinay Rajesh & Kulkarni, Kaustubh & Garg, Ashish & Gupta, Raju Kumar & Tiwari, Naveen & Nalwa, Kanwar Singh, 2022. "Recent advances in the modeling of fundamental processes in liquid metal batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).
    3. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    4. Tang, Xiaopeng & Gao, Furong & Zou, Changfu & Yao, Ke & Hu, Wengui & Wik, Torsten, 2019. "Load-responsive model switching estimation for state of charge of lithium-ion batteries," Applied Energy, Elsevier, vol. 238(C), pages 423-434.
    5. Chuanxue Song & Yulong Shao & Shixin Song & Cheng Chang & Fang Zhou & Silun Peng & Feng Xiao, 2017. "Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control," Energies, MDPI, vol. 10(3), pages 1-13, March.
    6. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    7. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    8. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    9. 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.
    10. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    11. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.

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