Diagnosing Conformal Coverage Failure Under Battery Deployment Shift for Joint State-of-Charge and State-of-Health Estimation

Conformal prediction guarantees interval coverage only when calibration and deployment data are exchangeable. We test joint state-of-charge (SOC) and state-of-health (SOH) intervals when this assumption fails across cells, discharge protocols, chemistries, and an electric-vehicle (EV) driving profile, with calibration data kept separate from training and model selection. At 90% nominal, SOC coverage is 90.9% on a random cycle split but 33.0–81.4% on held-out cells, at most 0.9% on cross-protocol windows with nonzero SOC, at most 5.16% across chemistries, and 0.02% on EV-profile cells (five-seed means). SOH coverage is below nominal under every shift. Four learned baselines (recurrent, convolutional, Transformer, and physics-informed) and an equivalent-circuit Kalman filter fall short on the hardest cells. Density-ratio and optimal-transport weighting, conformalised quantile regression, and group-conditional calibration reach nominal mean coverage on no held-out cell. Recalibrating on the deployed cell’s five latest labelled cycles restores coverage to 85.5–90.3% on held-out cells without retraining, but under protocol and EV-profile shift only with intervals over half the SOC range. Under chemistry shift, optimal-transport alignment configured on labelled target data lowers SOC error by 10–59%, yet only fine-tuning with target-domain calibration restores coverage (88.0–89.3%). Source-calibrated conformal intervals are not valid under deployment shift by default.