Instance-Based Transfer Learning-Improved Battery State-of-Health Estimation with Self-Attention Mechanism

Renjun He, Chunxiao Wang, Chun Yin, Shang Yang, Yifan Wang (), Yuanpeng Fang, Kai Chen and Jiusi Zhang ()
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Renjun He: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Chunxiao Wang: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Chun Yin: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Shang Yang: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yifan Wang: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yuanpeng Fang: AVIC Chengdu Aircraft Design & Research Institute, Chengdu 610091, China
Kai Chen: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Jiusi Zhang: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Energies, 2025, vol. 18, issue 21, 1-19

Abstract: Batteries’ state-of-health (SOH) estimation has attracted appealing attention in energy industrial systems. In conventional data-driven methods, the lack of target data and different source data can also lead to poor model training effect. To tackle this problem, this paper combines the instance-based transfer (ITL) and interpretable self-attention mechanism (SAM) to integrate the fitting ability of long short-term memory (LSTM), which can improve the SOH estimation performance. ITL re-weights the temporal instance of a training set to give more impact of target-like data, which can relax the independent and identical distribution (IID) assumption. SAM method can enhance the estimation performance by re-weighting the spatial features, and be interpreted by detailed visualization. During the model training, the pre-trained multi-layer LSTM model is fine-tuned by target data to make full use of target information. The proposed method has outperformed other compared algorithms in transfer tasks, and has tested in real-world cross-domain conditions datasets.

Keywords: instance transfer learning; batteries’ state-of-health estimation; long short-term memory; self-attention mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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