DOI: 10.12688/openreseurope.24549.1 ISSN: 2732-5121

Range-risk estimation for electric vehicles in low-coverage mountain road segments: A digital-twin study with prequential beacon-based Federated Learning

Yeray Mezquita, Jesús Emmanuel Vidal Cuevas, Diego Valdeolmillos, Albano Carrera
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Electric vehicles operating on isolated mountain roads may receive over-optimistic range estimates precisely where connectivity and charging alternatives are scarce. This paper proposes a beacon-centric Vehicle-to-Everything and Federated Learning architecture for range-risk estimation in low-coverage mountain-road segments. Entry and exit beacons maintain a local segment-specific model, broadcast it to approaching vehicles, and update it from traversal evidence contributed by vehicles after leaving the segment, without requiring raw telemetry to be centralised. The approach is evaluated in a digital twin of a 10.6 km fragment of the Puerto de la Quesera mountain pass using 12 simulated traversals from two electric-vehicle models under dry, wet and snow/low-temperature conditions. The evaluation is route-level, matching the operational decision made before entering the segment. Generic estimators systematically underestimate the energy required to complete the section: the nominal-consumption estimator produces a mean absolute error of 0.777 kWh, while a conventional non-local physics estimator produces 1.975 kWh. A segment-specific residual model reduces the leave-one-route-out error to 0.220 kWh. More importantly, the proposed alert-oriented evaluation shows that false-safe decisions are reduced from 14.58% for the non-local estimator and 8.33% for the nominal estimator to 1.04% for the segment-specific residual model. In the prequential beacon simulation, after eight previous vehicles, FedProx reduces the false-safe rate to 3.38%. These results support the feasibility of local, privacy-preserving range-risk intelligence for challenging road segments.

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