State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining

Kai Huang, Kaixin Yao, Yongfang Guo and Ziteng Lv

Energy, 2023, vol. 282, issue C

Abstract: Accurate state of health (SOH) estimation of lithium-ion batteries is essential to ensure the reliability of power equipment. However, the degradation trajectory of different cells and different types of batteries is not repeatable. At present, there is no unified model or method to effectively predict SOH for all batteries. Therefore, a new SOH estimation method is proposed in the paper. Firstly, two types of new features are proposed in this paper. One is the voltage features extracted from the constant-current charging stage, and the other is the capacity recovery feature. They are used to reflect the nonlinear degradation process of the battery. Secondly, the relationship between features and SOH is established by using the LSTM model, which can prevent the problem of gradient vanishing and gradient explosion during model learning. Finally, for the inconsistencies between the same type or different types of batteries, two different transfer learning strategies (fine-tuning and rebuilding) are proposed in this paper, and the effectiveness of the proposed features and transfer learning strategies is verified on three open-source battery data sets (NASA, Oxford, and CALCE). Experimental results show that the SOH estimation method proposed in the paper has good universality, robustness, and accuracy.

Keywords: Health feature mining; Lithium-ion batteries; Long short-term memory neural network; State of health estimation; Transfer learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223021333

DOI: 10.1016/j.energy.2023.128739

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