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arXiv · 2504.12444

Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning

Jiawei Zhang·Yu Zhang·Wei Xu·Yifei Zhang·Weiran Jiang·Qi Jiao·Yao Ren·Ziyou Song
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Abstract

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.

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Jiawei Zhang, Yu Zhang, Wei Xu, Yifei Zhang, Weiran Jiang, Qi Jiao, Yao Ren, Ziyou Song. 2025-04-16. Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning. https://arxiv.org/abs/2504.12444

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