A cycle-aware and physics-informed framework for battery remaining useful life prediction

Accurate prediction of the Remaining Useful Life (RUL) of Li-ion batteries is essential for safe and efficient energy systems, but it is difficult because sensor data are multivariate and irregularly sampled. Many state-of-the-art deep learning methods are domain-agnostic. For example, T-PATCHGNN uses generic patching that cuts charge-discharge cycles into fragments with little physical meaning, weakening the input signal and limiting learning of true degradation. We propose Bat-T-GNN, which injects domain knowledge at both the input and objective levels. First, Cycle-Aware Patching segments the time series by actual charge-discharge cycles, giving the model coherent, physically meaningful inputs. Second, a Physics-Informed Consistency Loss (PINN-RUL) regularizes training by requiring the final RUL prediction to be consistent with a physically plausible degradation curve learned from the data. This method experimented on public benchmarks show that proposed method significantly outperforms prior methods, including T-PATCHGNN. Ablation studies confirm that both proposed components drive the performance gains, establishing a new state of the art in battery RUL prediction.