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State of health estimation for lithium-ion batteries on few-shot learning

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  • Zhang, Shuxin
  • Liu, Zhitao
  • Su, Hongye

Abstract

State of health (SOH) is a critical indicator for implementing detection, diagnostics and prognostics on lithium-ion batteries. However, considering the difficulty of data collection and additional cost for gathering comprehensive field data in practical application, only limited data can be available for model establishment. In order to handle this insufficient data scenario, a novel Bayesian deep neural network has been established and validated on few-shot learning. Moreover, from the perspective of feature extraction, degradation patterns extracted from temporal cyclic discharge profiles are utilized for reflecting degradation mode and operation state, while the Gramian angular field is proposed for data distribution learning and information enhancement. Different percentages of data are conducted on model training to compare the comprehensive performance on various features and state-of-art methods with the proposed method on few-shot learning. Ultimately, experimental results prove better adaptability, generalization and effectiveness of the proposed method on lithium-ion battery SOH estimation regardless of data size.

Suggested Citation

  • Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223001202
    DOI: 10.1016/j.energy.2023.126726
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    6. Li, Fang & Min, Yongjun & Zhang, Ying & Zhang, Yong & Zuo, Hongfu & Bai, Fang, 2024. "State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
    8. Mizutani, Daijiro & Nakazato, Yuto & Ikushima, Rie & Satsukawa, Koki & Kawasaki, Yosuke & Kuwahara, Masao, 2024. "Optimal intervention policy of emergency storage batteries for expressway transportation systems considering deterioration risk during lead time of replacement," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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