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Joint estimation of SOC and peak power capability for series reused battery pack based on screening process method

Author

Listed:
  • Zhang, Yujie
  • Liu, Baicheng
  • Zhang, Hongguang
  • Kuang, Rao
  • Xu, Yonghong
  • Zhang, Jian
  • Yang, Fubin
  • Wang, Shuo

Abstract

Lithium-ion battery disposal is becoming an increasingly important issue with the rapid growth of Electric Vehicles (EVs) regarding resource conservation and environmental sustainability. It is considered the most suitable solution to reuse rather than dispose of retired batteries. However, the precision in estimating the battery states is of great importance to ensure the operational safety and efficiency of reused battery packs. This study proposes a joint estimation method to predict the State of Charge (SOC) and the peak power capability for reused battery packs considering inconsistency. The primary content of this work is described as follows. (1) This paper designs an improved screening method for evaluating the consistency of the reused batteries that are used to connect to the series battery pack. (2) A second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the reused battery pack. On this basis, the mean SOC is estimated by using Sage-Husa adaptive algorithm and extended Kalman filter (SH-AEKF), whereas the peak power capability is evaluated by considering multiple limitations. (3) An experiment is conducted to evaluate the robustness of the joint estimation method. The results show that the maximum absolute error of SOC estimation is below ±3 % while the mean absolute percentage error (MAPE) of peak power capability estimation could be limited to less than 3.5 %. This study indicates the high accuracy and reliability of the proposed joint estimation method for retired battery packs.

Suggested Citation

  • Zhang, Yujie & Liu, Baicheng & Zhang, Hongguang & Kuang, Rao & Xu, Yonghong & Zhang, Jian & Yang, Fubin & Wang, Shuo, 2024. "Joint estimation of SOC and peak power capability for series reused battery pack based on screening process method," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037186
    DOI: 10.1016/j.energy.2024.133940
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    References listed on IDEAS

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    1. Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    2. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    3. Gavin Harper & Roberto Sommerville & Emma Kendrick & Laura Driscoll & Peter Slater & Rustam Stolkin & Allan Walton & Paul Christensen & Oliver Heidrich & Simon Lambert & Andrew Abbott & Karl Ryder & L, 2019. "Recycling lithium-ion batteries from electric vehicles," Nature, Nature, vol. 575(7781), pages 75-86, November.
    4. Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    5. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Zhang, Youlang & Li, Yan & Tao, Yibin & Ye, Jilei & Pan, Aiqiang & Li, Xinzhou & Liao, Qiangqiang & Wang, Zhiqin, 2020. "Performance assessment of retired EV battery modules for echelon use," Energy, Elsevier, vol. 193(C).
    7. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    8. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    9. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).
    10. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    11. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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