Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning
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.
Cite
@article{arxiv.2504.12444,
title = {Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning},
author = {Jiawei Zhang and Yu Zhang and Wei Xu and Yifei Zhang and Weiran Jiang and Qi Jiao and Yao Ren and Ziyou Song},
journal= {arXiv preprint arXiv:2504.12444},
year = {2025}
}
Comments
This paper has been accepted for presentation at the 2025 IEEE Transportation Electrification Conference & Expo (ITEC)