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 battery degradation is modelled using lightweight LSTM networks, while global knowledge is learned through FedAvg-based aggregation without sharing raw data. To reflect practical forecasting conditions, the framework is evaluated under a forward-in-time validation protocol, where only early-life cycles are available during training. Extensive experiments demonstrate that DFL-RUL achieves accuracy comparable to or exceeding local and centralised baselines, while significantly reducing communication cost and training latency. Moreover, runtime profiling on EV-class edge hardware confirms low inference latency and low energy consumption, validating the framework’s suitability for on-device deployment. These results show that reliable battery RUL estimation can be achieved in a privacy-preserving, hardware-aware, and temporally robust federated setting.
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