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

arXiv: https://arxiv.org/abs/2504.12444 · HTML: https://mlsanity.com/paper/2504.12444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.03981/) (2021)

## Abstract (arXiv)

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.