ArticleScientific reports2026
Vehicle-wise validated ensemble learning framework for robust electric vehicle energy consumption prediction using physics-guided features and statistical benchmarking.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Predicting EV energy consumption accurately is essential for solving range anxiety and helping with better route planning and energy management in real conditions. Many current studies use random data splitting and few evaluation measures, which leads to overly optimistic results. Moreover, this approach does not properly test how well the models work on new data. According to the research requirements, this study presents a systematic ensemble learning framework using vehicle-wise data splitting to prevent information leakage and ensure realistic evaluation. Five machine learning models-Random Forest, ExtraTrees, XGBoost, LightGBM, and CatBoost-are tested using real electric vehicle data that includes only physics-based features for vehicle movement and environmental conditions. The model performance is evaluated using multiple metrics like MAE, RMSE, MAPE, R
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