Gas-Informed Machine Learning Framework for Stage Classification and Early Forecasting of Battery Degradation

profiles, we develop two complementary models: (i) a label-free PCA-K-means stage classifier that distinguishes linear and accelerated nonlinear degradation, and (ii) a single-shot forecaster that fuses a short capacity/SOH history with one OEMS-measured cycle to predict future capacity trajectories without autoregressive rollout. On held-out test cells, the stage classifier exhibits a 3.6% misclassification rate (5/136 OEMS-measured cycles) and flags the nonlinear transition 32.0 ± 17.2 cycles before the Bacon-Watts knee (18.0 ± 8.1% of lifetime). On held-out cells closest to the training protocol, the forecaster achieves 1.7-2.9% RMSE at 100-cycle horizons and reduces long-horizon drift relative to capacity-only baselines. These results establish gas evolution as a chemically grounded complement to electrical monitoring and motivate future integration with compact off-gas sensing for cycle-level battery health management.