Impact of Weather Conditions on Energy Consumption Modeling for Electric Vehicles

This study presents a methodology for developing an energy consumption model for electric vehicles based on dynamic vehicle and environmental data. Particular attention is given to analyzing the impact of ambient temperature on the energy consumption modeling. The approach leverages a large dataset to enhance model robustness while acknowledging the constraints imposed by the selected explanatory variables—vehicle speed and acceleration. To improve the model’s accuracy, temperature and acceleration data were clustered using the K-Means method, resulting in four distinct energy consumption models tailored to specific data clusters. Despite the inherent limitations of using only speed and acceleration as predictors, the proposed models achieved strong validation results, with an R2 value of 0.84 and a MAE ranging from 0.75 to 1.23 Wh. This approach enables microscale energy consumption prediction while ensuring broad applicability across various driving scenarios.