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

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Title: Instance-Based Transfer Learning-Improved Battery State-of-Health Estimation with Self-Attention Mechanism.
Authors: He, Renjun1,2,3 (AUTHOR), Wang, Chunxiao1,2 (AUTHOR), Yin, Chun1,3 (AUTHOR), Yang, Shang1 (AUTHOR), Wang, Yifan1,2 (AUTHOR), Fang, Yuanpeng2,3 (AUTHOR), Chen, Kai1 (AUTHOR), Zhang, Jiusi1 (AUTHOR)
Source: Energies (19961073). Nov2025, Vol. 18 Issue 21, p5672. 19p.
Subjects: Transfer of training, Long short-term memory, Data fusion (Statistics), Machine learning
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. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Instance-Based Transfer Learning-Improved Battery State-of-Health Estimation with Self-Attention Mechanism.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Nov2025, Vol. 18 Issue 21, p5672. 19p.
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2025
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