Context-Aware Risk-Constrained Routing for Battery Electric Vehicles: Probabilistic Range and Arrival-Time Assurance in Dense Urban Networks

This study aims to evaluate whether context-dependent prediction and an explicit arrival-reserve chance constraint improve the reliability of battery electric vehicle (BEV) route decisions. The framework combines a physics-based energy backbone, grouped out-of-fold residual learning, split-conformal intervals and paired estimated-time-of-arrival (ETA)–energy scenarios. A controlled 12-week semi-synthetic replay contains 500 trips with Tesla Model Y and Volkswagen ID.7 parameterisations. In the event/pedestrian/disruption regime, ETA mean absolute error (MAE) is 0.74 min for the Model Y and 0.75 min for the ID.7 under full context, compared with 17.31 and 15.92 min under reduced context. Across all 1980 candidate routes and both vehicles, the nominal 90% full-context ETA intervals cover 91.2% of realised values. These large prediction gains are conditional on a generator whose designated delay drivers are visible to the full-context model. Relative to time-only routing, reserve violations decrease from 22.0% to 5.2% and from 19.2% to 5.6%, with increases in ratios of mean travel-time of 15.0% and 15.6%, respectively. However, the chance-constrained and quantile-robust policies choose different routes on only 3 of 500 trips and have identical realised violation indicators. The evidence supports explicit risk and fallback reporting within the replay.