通过群体学习提升数据稀缺下的电池容量估计精度。
Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning
- 采用去中心化群体学习框架，多设备协作建模而不共享原始数据。
- 在四种数据受限场景中均提升估计精度，接近集中式学习效果。
- 适合电池管理、车联网等需隐私保护的分布式应用场景。
数据驱动方法在电动汽车电池管理任务（如容量估计）中展现出潜力，但在数据有限场景下性能受限。跨开发者共享电池数据可提升模型准确性与泛化能力，但兼具数据隐私与容错性的有效管理框架仍缺失。本文提出一种群体电池管理系统，结合去中心化群体学习（SL）框架与可信度加权模型融合机制，在保障数据隐私与安全的前提下，提升数据受限场景中的电池容量估计性能。在包含66个商用LiNiCoAlO2电池、覆盖多种工况循环的数据集上验证了该框架有效性。具体在四种数据受限情形（数据均衡、容量偏斜、特征偏斜、质量偏斜）下进行测试，结果表明SL在所有情况下均提升了估计精度，并达到与大规模数据集中学习相当的水平。
原文摘要 · Abstract (English)
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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