A Single Leaf Heralds Autumn: Predicting Battery Lifetime under Stationarity Masking

Early battery lifetime prediction estimates long-term cycle life from limited initial cycles, reducing the time required for full degradation testing. However, early cycles are highly similar, making subtle degradation variations difficult to identify from individual cycle states. Beyond marginal distribution shifts in time-series non-stationarity, we characterize a regime in which near-stationary marginals coexist with variations in conditional transitions between adjacent cycles, which we term stationarity masking. To isolate these hidden transition variations from marginal effects, we disentangle marginal and conditional discrepancies and derive a unique transition projection that preserves the marginal distributions of both adjacent cycles while capturing their inter-cycle transition structure. Building on this analysis, we propose LEAF, a simple yet effective framework for early battery lifetime prediction. LEAF learns conditional associations between local states of adjacent cycles, extracts conditional transitions through fixed-marginal projection, and models their sequential evolution for lifetime prediction. Extensive experiments on 16 datasets spanning Li-ion, Zn-ion, and Na-ion batteries validate the effectiveness of LEAF across diverse battery systems and aging conditions. Code is available at https://anonymous.4open.science/r/Batt_LEAF/.