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Fast EIS acquisition method based on SSA-DNN prediction model

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  • Chang, Chun
  • Pan, Yaliang
  • Wang, Shaojin
  • Jiang, Jiuchun
  • Tian, Aina
  • Gao, Yang
  • Jiang, Yan
  • Wu, Tiezhou

Abstract

Electrochemical impedance spectroscopy (EIS) is an efficient and information-rich technique for detecting lithium-ion batteries. However, the measurement of EIS takes much time, and the lower the measurement frequency, the longer the measurement takes. To address this problem, this study innovatively proposes an EIS prediction method based on a sparrow search algorithm optimized deep neural network (SSA-DNN). The overall measurement time is reduced by extracting features from the medium-high frequency segments, where the EIS measurement is less time-consuming, and predicting the medium-low frequency segments that consume more measurement time. After evaluating the EIS prediction results at different cycling temperatures and states of charge (SOC), it is concluded that the EIS prediction method proposed in this paper has the advantages of fast measurement speed, high accuracy and applicability. Finally, the predicted EIS is used to estimate the state of health (SOH), and the distribution of relaxation time (DRT) is calculated. The results show that the proposed EIS prediction method has a maximum prediction RMSE of 29.15 mΩ, and the measurement time is reduced to 2.94 % of the original measurement time, which can be widely used in various scenarios based on EIS technology.

Suggested Citation

  • Chang, Chun & Pan, Yaliang & Wang, Shaojin & Jiang, Jiuchun & Tian, Aina & Gao, Yang & Jiang, Yan & Wu, Tiezhou, 2024. "Fast EIS acquisition method based on SSA-DNN prediction model," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031626
    DOI: 10.1016/j.energy.2023.129768
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    References listed on IDEAS

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    1. Wu, Tingting & Wang, Changhong & Hu, Yanxin & Liang, Zhixuan & Fan, Changxiang, 2023. "Research on electrochemical characteristics and heat generating properties of power battery based on multi-time scales," Energy, Elsevier, vol. 265(C).
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    3. Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    4. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    5. He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
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