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Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method

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  • Ma, Yan
  • Shan, Ce
  • Gao, Jinwu
  • Chen, Hong

Abstract

Accurate state of health (SOH) estimation of lithium-ion battery provides a guarantee for the safe driving of electric vehicles. Most SOH estimation methods based on the machine learning assume that the training and testing data follow the uniform distribution. However, the distribution of the datasets obtained at the different working conditions has discrepancy, which also increases its inherently large computational burden. Therefore, a novel SOH estimation method based on multiple health indicators (HIs) fusion using transfer learning and deep belief network (DBN)-long short-term memory (LSTM) hybrid network is proposed. Transfer learning is used to learn the shared features in the source domain and the target domain. Then, aiming at the insufficiency of shallow network in mining data features, DBN is utilized for SOH estimation. And considering the influence of historical information on future prediction, LSTM cell is used to replace the traditional BP neural network structure. Comparative study is conducted by applying deep and shallow network on the measured data for monitoring SOH of the battery in applications. The experimental results show that the method proposed in this paper is effective, and the performance of knowledge transferring under single domain and cross domain is also verified.

Suggested Citation

  • Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004379
    DOI: 10.1016/j.ress.2022.108818
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    6. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).

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