Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909582146666496 |
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| author | Zhang, Jiawei Zhang, Yu Xu, Wei Zhang, Yifei Jiang, Weiran Jiao, Qi Ren, Yao Song, Ziyou |
| author_facet | Zhang, Jiawei Zhang, Yu Xu, Wei Zhang, Yifei Jiang, Weiran Jiao, Qi Ren, Yao Song, Ziyou |
| contents | Data-driven methods have shown potential in electric-vehicle battery management tasks such as capacity estimation, but their deployment is bottlenecked by poor performance in data-limited scenarios. Sharing battery data among algorithm developers can enable accurate and generalizable data-driven models. However, an effective battery management framework that simultaneously ensures data privacy and fault tolerance is still lacking. This paper proposes a swarm battery management system that unites a decentralized swarm learning (SL) framework and credibility weight-based model merging mechanism to enhance battery capacity estimation in data-limited scenarios while ensuring data privacy and security. The effectiveness of the SL framework is validated on a dataset comprising 66 commercial LiNiCoAlO2 cells cycled under various operating conditions. Specifically, the capacity estimation performance is validated in four cases, including data-balanced, volume-biased, feature-biased, and quality-biased scenarios. Our results show that SL can enhance the estimation accuracy in all data-limited cases and achieve a similar level of accuracy with central learning where large amounts of data are available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning Zhang, Jiawei Zhang, Yu Xu, Wei Zhang, Yifei Jiang, Weiran Jiao, Qi Ren, Yao Song, Ziyou Systems and Control Machine Learning Chemical Physics Data-driven methods have shown potential in electric-vehicle battery management tasks such as capacity estimation, but their deployment is bottlenecked by poor performance in data-limited scenarios. Sharing battery data among algorithm developers can enable accurate and generalizable data-driven models. However, an effective battery management framework that simultaneously ensures data privacy and fault tolerance is still lacking. This paper proposes a swarm battery management system that unites a decentralized swarm learning (SL) framework and credibility weight-based model merging mechanism to enhance battery capacity estimation in data-limited scenarios while ensuring data privacy and security. The effectiveness of the SL framework is validated on a dataset comprising 66 commercial LiNiCoAlO2 cells cycled under various operating conditions. Specifically, the capacity estimation performance is validated in four cases, including data-balanced, volume-biased, feature-biased, and quality-biased scenarios. Our results show that SL can enhance the estimation accuracy in all data-limited cases and achieve a similar level of accuracy with central learning where large amounts of data are available. |
| title | Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning |
| topic | Systems and Control Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2504.12444 |