WeatherRobustBus cuts electric bus winter failure probability by 85%
Cabin heating depletion causes 76% failure rate on cold days—new framework fixes it.
Yifan Wang (University of Toronto) presents WeatherRobustBus, an open-data framework addressing a critical blind spot in electric bus fleet electrification: cold-weather cabin heating can drain batteries faster than scheduled layovers can recharge, causing cascading timetable failures. The framework couples a physics-based traction and cabin-thermal model with a bounded monotone residual ensemble to capture cold-weather energy uncertainty. Validated against an independent EnergyPlus bus-cabin simulation driven by real Toronto weather, it achieves the lowest all-year error (0.213 kWh RMSE over 8,760 hours) and remains reliable in the out-of-support cold tail (≤ -12°C), where pure machine-learning baselines degrade by 1.5–4x.
Embedded in a Monte Carlo simulator over 60 real Toronto TTC vehicle blocks, WeatherRobustBus reveals a sharp weather-induced failure envelope. On eight cold-wave days, mean failure probability was 0.759. A robust policy combining opportunity charging, a fuel-fired cabin-heating bridge, and modest buffering reduces that to 0.112—an 85% reduction. Ablation shows opportunity charging is the dominant lever; the heater is a low-cost complement. The framework provides a reproducible pathway from weather data to winter-resilience decisions for electric bus fleets.
- Physics-anchored ML achieves 0.213 kWh RMSE over 8,760 hours vs. pure ML baselines that degrade 1.5–4x below -12°C.
- Identified cold-wave failure probability of 0.759 from cabin heating depletion; robust policy reduces it to 0.112.
- Dominant lever is opportunity charging; fuel-fired cabin heater offers low-cost complementary resilience.
Why It Matters
A data-driven blueprint for transit agencies to electrify winter operations without sacrificing reliability.