FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data

Rucong Lai, Jie Wang, Yong Tian and Jindong Tian

Applied Energy, 2024, vol. 368, issue C, No S0306261924009176

Abstract: State of Charge (SOC) estimation of lithium-ion batteries using deep learning methods has attracted considerable attention. However, existing approaches predominantly rely on centralized learning paradigms, necessitating datasets generated under diverse conditions. This process can be time-consuming and even impractical for real-world applications due to data privacy. In this study, we propose a Federated-Learning-Based Collaborative Battery Estimation system (FedCBE) for SOC estimation of lithium-ion batteries. The proposed FedCBE leverages federated learning (FL), which requires only local model weights rather than raw data for global model aggregation, thereby enhancing the model’s training efficiency and mitigating data privacy concerns. Additionally, we propose label normalization, proximal term, and shared-data strategies to avoid weight divergences encountered during FL training, thus improving the stability and generalization of the proposed FedCBE. Both open-source and our laboratory battery datasets are employed to demonstrate the efficacy of the FedCBE. The results reveal that the global model trained on open-source datasets achieves RMSE under 4.5% on our laboratory battery datasets.

Keywords: Lithium-ion battery; Federated learning; Long short-term memory; State-of-charge estimation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924009176
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:368:y:2024:i:c:s0306261924009176

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123534

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().