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A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

Author

Listed:
  • Haochen Qin

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

  • Xuexin Fan

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

  • Yaxiang Fan

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

  • Ruitian Wang

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

  • Qianyi Shang

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

  • Dong Zhang

    (National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)

Abstract

High maintenance costs and safety risks due to lithium-ion battery degeneration have significantly and seriously restricted the application potential of batteries. Thus, this paper proposes an efficient calculation approach for state of health (SOH) estimation in lithium-ion batteries that can be implemented in battery management system (BMS) hardware. First, from the variables of the charge profile, only the complete voltage data is taken as the input to represent the complete aging characteristics of the batteries while limiting the computational complexity. Then, this paper combines the light gradient boosting machine (LightGBM) and weighted quantile regression (WQR) methods to learn a nonlinear mapping between the measurable characteristics and the SOH. A confidence interval is applied to quantify the uncertainty of the SOH estimate, and the model is called LightGBM-WQR. Finally, two public datasets are employed to verify the proposed approach. The proposed LightGBM-WQR model achieves high accuracy in its SOH estimation, and the average absolute error (MAE) of all cells is limited to 1.57%. In addition, the average computation time of the model is less than 0.8 ms for ten runs. This work shows that the model is effective and rapid in its SOH estimation. The SOH estimation model has also been tested on the edge computing module as a possible innovation to replace the BMS bearer computing function, which provides tentative solutions for online practical applications such as energy storage systems and electric vehicles.

Suggested Citation

  • Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5414-:d:1195363
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    References listed on IDEAS

    as
    1. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    2. Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
    3. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    4. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    5. Shuxiang Song & Chen Fei & Haiying Xia, 2020. "Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction," Energies, MDPI, vol. 13(4), pages 1-13, February.
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    Cited by:

    1. Xiuli Wang & Junkai Wei & Fushuan Wen & Kai Wang, 2023. "A Trading Mode Based on the Management of Residual Electric Energy in Electric Vehicles," Energies, MDPI, vol. 16(17), pages 1-23, August.
    2. Jiang, Fusheng & Ren, Yi & Tang, Ting & Wu, Zeyu & Xia, Quan & Sun, Bo & Yang, Dezhen, 2024. "An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).

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