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Vehicle-wise validated ensemble learning framework for robust electric vehicle energy consumption prediction using

T Mariprasath1, Preethi Srinivasan2, Geetha Anbazhagan3

  • 1Department of EEE, K.S.R.M College of Engineering (Autonomous), Kadapa, 516005, Andhra Pradesh, India. ts.mariprasath@gmail.com.

Scientific Reports
|June 4, 2026
PubMed
Summary

Accurate electric vehicle (EV) energy consumption prediction is crucial. This study introduces a robust framework using vehicle-wise data splitting and ensemble learning, with ExtraTrees showing superior performance for reliable range estimation.

Keywords:
Electric vehicle energy consumptionEnsemble learningExtraTreesMachine learningPhysics-guided featuresStatistical validationVehicle telemetry dataVehicle-wise validation

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Area of Science:

  • Energy Systems Engineering
  • Machine Learning Applications
  • Automotive Engineering

Background:

  • Accurate electric vehicle (EV) energy consumption prediction is vital for addressing range anxiety and optimizing route planning.
  • Current prediction methods often suffer from overly optimistic results due to random data splitting and limited evaluation metrics.
  • A realistic evaluation is necessary to assess model performance on unseen data.

Purpose of the Study:

  • To develop a systematic ensemble learning framework for accurate EV energy consumption prediction.
  • To ensure realistic model evaluation by employing vehicle-wise data splitting, preventing information leakage.
  • To compare the performance of five distinct machine learning models using physics-based features.

Main Methods:

  • Implemented an ensemble learning framework with vehicle-wise data splitting.
  • Tested five machine learning models: Random Forest, ExtraTrees, XGBoost, LightGBM, and CatBoost.
  • Utilized real-world EV data incorporating physics-based features for vehicle movement and environmental conditions.
  • Evaluated model performance using MAE, RMSE, MAPE, R², and EVS metrics.
  • Performed statistical significance testing with Friedman and Wilcoxon tests.

Main Results:

  • The ExtraTrees model achieved the highest performance, with R² = 0.9407, RMSE = 0.6113 kWh/km, and MAPE = 7.62%.
  • ExtraTrees demonstrated consistent accuracy across diverse driving conditions.
  • Statistical analysis confirmed ExtraTrees significantly outperformed LightGBM, with comparable results to other ensemble models.
  • Ensemble methods effectively reduced prediction errors for various EV data types.

Conclusions:

  • The proposed framework offers a dependable solution for predicting EV energy consumption and range.
  • The ExtraTrees model, within the ensemble framework, provides a robust approach for real-world applications.
  • Vehicle-wise data splitting is critical for realistic performance evaluation in EV energy consumption studies.