Decentralized Deep Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning

Kei Long Wong, Rita Tse, Su Kit Tang, Giovanni Pau

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Transportation Electrification
Publication statusAccepted/In press - 2024


  • battery degradation
  • Data models
  • data-driven
  • deep learning
  • Degradation
  • federated learning
  • Federated learning
  • Forecasting
  • Lithium-ion batteries
  • Lithium-ion battery
  • Predictive models
  • state of health forecasting
  • Training


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