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State of health prognostics for series battery packs: A universal deep learning method

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  • Che, Yunhong
  • Deng, Zhongwei
  • Li, Penghua
  • Tang, Xiaolin
  • Khosravinia, Kavian
  • Lin, Xianke
  • Hu, Xiaosong

Abstract

Prognostic and health management for battery packs depend greatly on the accurate and efficient state of health prognosis. This paper proposed a strategy for generating universal health indicators and model fusion method. Several health indicators that reflect the integral characteristic and information distribution are generated and proved with high correlation with capacities. The generation method can be adapted for various battery packs regardless of the battery types, connected cell numbers, and aging statuses. Then the generation method is extended to dynamic working conditions by combining the mean plus difference model and recurrent least-square online parameter identification. Thanks to the uniform feature input and state of health output, a universal prognostic model is constructed with deep learning frameworks. The prognostic performance is further improved by the model migration and fusion, which extend the application area to predict the state of health for different battery packs and/or under different working conditions. Experimental results show that the prognostic model can be implemented among various battery packs with satisfactory accuracy and reliability. The mean absolute errors and root mean square errors are less than 2.5% and 3.1%, respectively, under various application occasions.

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  • Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221021058
    DOI: 10.1016/j.energy.2021.121857
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    3. Muhammad Waseem & Jingyuan Huang & Chak-Nam Wong & C. K. M. Lee, 2023. "Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    4. Jin, Haiyan & Cui, Ningmin & Cai, Lei & Meng, Jinhao & Li, Junxin & Peng, Jichang & Zhao, Xinchao, 2023. "State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    5. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    6. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    7. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    8. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    9. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    10. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    11. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).
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