A Physics-Guided Framework for Available Traction Power and In-Motion Charging

This paper presents a physics guided learning framework for forecasting available traction power (ATP) and quantifying in motion charging (IMC) headroom in catenary fed trolleybus networks. The method combines a chronologically split, leak free feature policy, monotone gradient boosting with regime aware experts, an LSTM sequential baseline, and conformalized quantile regression for calibrated prediction intervals. Two case studies are reported. The first uses 58 345 high frequency samples from a dual power trolleybus in Malatya, Türkiye, where the monotone LightGBM reaches R2 = 0.998 with mean absolute error around 1.3 kW and workload weighted error below 0.24% under a chronological split. The second uses the ZTBus dataset from Zurich, Switzerland, covering 1 409 missions, two vehicles, and 3.5 years of service. On the Zurich data, an LSTM baseline outperforms the tree based models (R2 = 0.956, RMSE = 13.9 kW versus 17.0 kW for constrained LightGBM), while on the single run Malatya scenario the tree models dominate. A monotone constraint ablation shows that physics guided regularization slightly reduces in distribution accuracy but improves cross vehicle generalization. Conformal calibration delivers 90% prediction intervals with 93% empirical coverage on the Malatya dominant band and 79 to 81% on ZTBus under temporal distribution shift. Using the calibrated lowerATP quantile as a conservative headroom estimate, a 20 to 100 kW in motion charger recovers more than 99.8%of the oracle energy over the observed 31.0 km Malatya trip. These results show that the framework generalizes across cities, vehicles, and target variables, and can support in motion charging assessment in trolleybus networks.