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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en18215672 |
