Decentralized Deep-Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning
Open accessAbstract
The rapid advancement of deep learning techniques has expedited the progress of data-driven forecasting methods for lithium-ion battery health. The conventional deep learning techniques for battery health forecasting require the training and refining of the predictive model in a centralized manner. However, centralized approaches face challenges related to data privacy and scalability. Therefore, it is essential to explore a decentralized methodology for the forecasting of battery health in order to safeguard privacy, utilize onboard computing resources, and facilitate the rapid integration of new data. This article proposes the utilization of federated learning to train a lithium-ion battery health forecasting model in a decentralized manner. All the experiments carried out in this study have been specifically customized to align with real-world conditions. A client selection strategy designed specifically for battery health forecasting is presented, which is demonstrated to increase accuracy throughout the training process. The evaluation results show that the predictive model trained in a decentralized manner exhibits comparable overall performance to the centralized counterpart.
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Wong et al. employed an LSTM-based ED structure for LIB SOH prediction, demonstrating that their decentralized model performed similarly to centralized approaches.
“Wong et al. employed an LSTM-based ED structure for LIB SOH prediction, demonstrating that their decentralized model performed similarly to centralized approaches[127].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [127]
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- Authors
- Wong, K. L., Tse, R., Tang, S.-K., & Pau, G.
- Affiliations
- Macao Polytechnic University (MO)
- University of California, Los Angeles (US)
- Venue
- IEEE Transactions on Transportation Electrification
- Published
- 2024-12
- Volume
- 10
- Issue
- 4
- Pages
- 8199-8212
- ISSN
- 2332-7782 (ISSN-L)
- 2372-2088
- OpenAlex
- W4390887437
- Cited by
- 38
- References
- 47
- Citations by year
- 2026: 20 · 2025: 14 · 2024: 4
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Transactions on Transportation Electrification (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Funders
- Horizon 2020 Framework Programme
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- lithium-ion batteries, federated learning, decentralized deep learning, data privacy, on-board processing, battery state of health estimation
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