DFL-RUL: Decentralised Federated Learning for Battery Remaining Useful Life Estimation on Heterogeneous Edge-to-cloud

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is essential for reliable and cost-effective electric vehicle operation, yet existing approaches largely rely on centralised training or overlook deployment constraints and data heterogeneity. This paper introduces DFL-RUL, a decentralised federated learning framework specifically designed to address feature-space inconsistency, temporal generalisation, and edge-level feasibility in real-world battery prognostics. Unlike prior federated RUL methods that assume aligned feature representations across clients, DFL-RUL integrates unsupervised, client-side PCA to automatically align heterogeneous sensor features before model aggregation. Local…

• MO
  Ministry of Trade, Industry and Energy
• KE
  Korea Evaluation Institute of Industrial Technology

  Award: RS-2024-00436695
• KI
  Korea Institute of Energy Technology Evaluation and Planning