Mission-Phase Feature Learning for eVTOL Li-Ion Battery Prognostics: A Leakage-Safe Cell-Held-Out Benchmark for SOC, SOH, and RUL

Reliable battery prognostics for electric vertical take-off and landing (eVTOL) aircraft require models that preserve phase-dependent electrothermal information while generalizing to cells absent from training. This study reconstructs the public CMU eVTOL battery dataset and establishes a leakage-safe benchmark for state of charge (SOC), five-mission-ahead state of health (SOH), and threshold-based remaining useful life (RUL). A protocol-based screen identified 441 valid C/5 reference-performance-test anchors across 22 cells; three cells with non-monotone diagnostic-capacity trajectories were excluded from the primary health benchmark, leaving 19 cells. Predictors were restricted to telemetry-derived phase and mission features, with cell identity and target- or future-derived quantities excluded. All health models were evaluated using outer leave-one-cell-out validation with matched 20-mission histories. Random Forest achieved the lowest SOH MAE of 0.620 percentage points, compared with 1.252 for the mission-phase Transformer and 1.471 for Attention-LSTM-MoE. The best RUL MAEs were 24.067, 46.527, and 122.198 missions at the 90%, 85%, and 80% SOH thresholds. Cell-bootstrap uncertainty showed that model differences were threshold-dependent. Row-random splitting produced substantially more optimistic errors than cell-held-out evaluation. These results show that rigorous target construction and leakage-safe validation are critical and that increased sequence-model complexity does not guarantee superior unseen-cell generalization.