What question did this study set out to answer?

To develop a predictive model that accurately estimates the energy consumption of electric vehicles using machine learning.

January 26, 2026

An Explainable Ensemble Machine Learning Method for Electric Vehicles Energy Consumption Rate Estimation

Key Points

To develop a predictive model that accurately estimates the energy consumption of electric vehicles using machine learning.
Utilized the Spritmonitor public real-world dataset for model training.
Conducted data preprocessing before applying the ensemble method.
Employed Gradient Boosting Regression Trees for energy consumption prediction.
Applied SHAP analysis to identify influential factors affecting energy consumption.
Achieved an R2 of 1.0 for training and 0.99 for testing, indicating high prediction accuracy.
Obtained a mean absolute error (MAE) of 0.08 and root mean square error (RMSE) of 0.16, outperforming baseline models.
Identified key factors influencing energy consumption such as driving range, speed, and state of charge.

Abstract

ABSTRACT The rapid adoption of electric vehicles (EVs) highlights the need for intelligent systems to improve energy efficiency and optimize driving range. Since energy consumption and driving range modeling are closely related, understanding the energy consumption (EC) of EVs can provide essential insights to drivers and reduce “range anxiety.” Previous studies have relied on traditional analytical and statistical methods, which lack the representativeness of influential factors and the interpretability of the model applied in EC modeling. To address this issue, we propose an explainable ensemble machine learning model to predict EC of EVs, considering the most important features and the factors that exhibit greater influence on EC. The Spritmonitor public real‐world dataset is used for this study. First, data preprocessing is conducted before feeding data into the ensemble method. Second, the Energy Consumption Rate (ECR) was predicted using Gradient Boosting Regression Trees (GBRT). The proposed predictive framework demonstrates superior prediction accuracy compared to baseline models. GBRT achieved the highest R 2 (1 and 0.99 for training and testing, respectively) and the lowest MAE (0.08) and RMSE (0.16) compared to other models, including XGBoost, LightGBM, and CatBoost. Finally, SHAP (Shapley Additive exPlanations) analysis was applied to explain the proposed model and identify the most influential dynamics factors, including driving range, capacity, speed, state of charge (SOC), ambient temperature, road type, driving style, air conditioning, and heating usage. The results suggest that the proposed framework can effectively enhance the prediction of the EC of EVs and facilitates the analyze driving factors, thereby supporting intelligent trip planning, adaptive energy‐aware management in transportation systems and provide insightful feedback to drivers.

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