Predicting Auxiliary Energy Demand in Electric Vehicles Using Physics-Based and Machine Learning Models

Auxiliary systems, particularly HVAC and thermal management, significantly influence electric vehicle (EV) range under diverse weather conditions. Accurate prediction of auxiliary power demand remains challenging due to nonlinear temperature dependencies and driving dynamics. Here we develop an integrated physics-based decomposition combined with an XGBoost machine learning model trained on 95,028 real-world measurements from EVs operating across multi-seasonal conditions (−8 °C to +33.5 °C). The model achieves an R² of 0.9986 and a mean absolute error of 35 W, revealing that auxiliary loads contribute variably from 75% while idle to 12% during highway driving, with heating power dominating cooling by a 7:1 ratio and increasing 44-fold at low temperatures. Feature importance analysis identifies accelerator pedal position and heating efficiency per temperature differential as primary predictors, indicating coupling between propulsion and auxiliary loads. These findings underscore the necessity of context-aware auxiliary power prediction to enhance EV energy management and range forecasting, particularly in cold climates where heating demands critically impact efficiency.