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A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles

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  • Li, Zhirun
  • Xiong, Rui
  • Mu, Hao
  • He, Hongwen
  • Wang, Chun

Abstract

To improve the estimation accuracy of a battery’s inner state for a battery management system, an improved online model-based parameter identification algorithm is proposed. To reduce the computation cost, the existing methods regard the open circuit voltage over a certain time as a constant value. However, the battery state-of-charge (SoC) estimation error with the traditional method will deteriorate with larger sampling intervals. Compared with the existing parameter identification method, a new online estimation method is proposed, and both recursive least squares (RLS) and least mean square (LMS) algorithms are employed and compared systematically. The LMS algorithm, which requires less computational capability and storage space but performs worse than the RLS algorithm, is also invalid for the wide sampling interval in the traditional method. The improved method using LMS can maintain the maximum SoC estimation error at less than 10%. The simulation results show that the proposed approach can accurately identify the model parameters within 5% SoC estimation error. Finally, a hardware-in-the-loop validation experiment is carried out to prove the accuracy and superiority of the improved method.

Suggested Citation

  • Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    • Handle: RePEc:eee:appene:v:207:y:2017:i:c:p:363-371
    DOI: 10.1016/j.apenergy.2017.05.081
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    3. Xiao, Feiyu & Xing, Bobin & Kong, Lingzhao & Xia, Yong, 2021. "Impedance-based diagnosis of internal mechanical damage for large-format lithium-ion batteries," Energy, Elsevier, vol. 230(C).
    4. Xu, Maoshu & Zhang, E. & Wang, Sheng & Shen, Yi & Zou, Binchen & Li, Haomiao & Wan, Yiming & Wang, Kangli & Jiang, Kai, 2024. "Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures," Applied Energy, Elsevier, vol. 355(C).
    5. Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    6. Tang, Aihua & Huang, Yukun & Xu, Yuchen & Hu, Yuanzhi & Yan, Fuwu & Tan, Yong & Jin, Xin & Yu, Quanqing, 2024. "Data-physics-driven estimation of battery state of charge and capacity," Energy, Elsevier, vol. 294(C).
    7. Jiang, Yihui & Xu, Jun & Liu, Mengmeng & Mei, Xuesong, 2022. "An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules," Energy, Elsevier, vol. 259(C).
    8. Damoon Soudbakhsh & Mehdi Gilaki & William Lynch & Peilin Zhang & Taeyoung Choi & Elham Sahraei, 2020. "Electrical Response of Mechanically Damaged Lithium-Ion Batteries," Energies, MDPI, vol. 13(17), pages 1-15, August.
    9. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.

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