M5Boost: A Machine Learning Approach for Driving Range Estimation in Electric Vehicles Considering Battery-Related Factors

Hybrid attention-enhanced physics-informed neural framework for accurate electric vehicle range prediction

Combining Statistical and Machine Learning Methodologies in Energy Consumption Forecasting for Electric Vehicles

Achieving the Sustainable Development Goals (SDGs) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, reliably predicting energy consumption under highly variable real-world driving conditions and across different temporal horizons remains a critical challenge regarding the widespread adoption of Electric Vehicles (EVs), directly impacting the precision of range estimation, route planning, and charging strategies. To address this, a novel approach is proposed, combining advanced machine learning models—such as XGBoost, Random Forest, and regression-based techniques—with innovative dataset manipulation using statistical methods. The methodology integrates feature engineering to incorporate vehicle-specific metrics, including driving patterns and environmental conditions, ensuring that models dynamically adapt to real-world scenarios. The proposed framework demonstrates high accuracy and robustness in predicting energy consumption, providing valuable insights for sustainable transportation and efficient energy management toward SDG achievement.

Data-Driven Analysis of Driving Patterns via Vehicle Telemetry for Enhanced ADAS Decision-Making

Electric Vehicle Energy Consumption Prediction: A Physics-Informed Machine Learning Approach

The increasing adoption of electric vehicles (EVs) as a sustainable alternative to internal combustion engine vehicles has intensified the need for accurate and interpretable energy consumption prediction models to support vehicle design, battery management, and charging infrastructure planning. This study presents a physics-informed machine learning framework for predicting EV energy consumption using a dataset of approximately 300 electric vehicle models sourced from publicly available vehicle specifications. A reduced-order physical model derived from the work–energy theorem and Newtonian mechanics was developed to bridge classical vehicle dynamics theory with data-driven modeling, incorporating vehicle mass, aerodynamic drag coefficient, and the mass-to-battery-capacity ratio as physically meaningful input features. Five regression algorithms Linear Regression, Random Forest, XGBoost, LightGBM, and Support Vector Regression were implemented and evaluated under a consistent 5-fold cross-validation framework using R², RMSE, and MAE as performance metrics. Random Forest achieved the highest predictive accuracy (R² = 0.841, RMSE = 1.593 kWh/100 km), followed by Linear Regression (R² = 0.837) and XGBoost (R² = 0.820), while LightGBM and SVR demonstrated substantially weaker performance. Feature importance analysis confirmed that battery capacity, vehicle mass, and driving range are the most influential predictors, consistent with the physics-informed framework and recent literature. The convergence between data-driven findings and physical interpretations validates the proposed approach as a robust, transparent, and scalable tool for EV energy modeling, with direct applications in sustainable transportation planning and evidence-based energy policy.

A Comparative Machine Learning Approach for Energy Consumption Prediction in Hybrid Vehicles

With the rise in environmental concerns and global shift toward sustainable mobility, accurately predicting energy consumption in hybrid vehicles (HVs) is essential for reliable transportation planning and optimizing efficient energy management. This study employs the National Household Travel Survey (NHTS) dataset to develop predictive models for estimating energy usage in HVs. In this paper, four machine learning models (XGBoost, Linear Regression, Random Forest, and Neural Network) are implemented to analyze and compare their effectiveness in forecasting energy consumption. The dataset is preprocessed and standardized, followed by model training and 5-fold cross-validation. The performance of each model is determined using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²). Among them, the Random Forest model achieved the best effectiveness with lower RMSE and MAE along with the highest R2 score for predicting energy consumption in hybrid vehicles. The findings show the prospective of machine learning techniques, particularly the Random Forest model, which delivers the highest accuracy in predicting hybrid vehicle energy consumption. This result supports more informed decision-making for developing eco-friendly transport solutions, reducing fuel consumption and emissions.

Development and Validation of a Range Prediction Model for New Energy Vehicles

Given the significant discrepancy between the official driving range of new energy vehicles and users' real-world experience, this paper analyzes the range attainment rate by considering three key factors: ambient temperature, driving speed, and air-conditioning operation. To address this issue, this study collects and preprocesses real-world test samples from media platforms such as Autohome and Dongchedi, combines them with official test reports from the China Automotive Technology and Research Center (CATARC), and constructs a range prediction model based on multiple linear regression. The empirical results reveal that ambient temperature acts as the dominant factor influencing driving range variation, contributing to nearly 40% of total range attenuation, while high-speed driving and air-conditioning operation also significantly increase vehicle energy consumption. Across several test scenarios, the coefficient of determination (R²) remains at 0.84, and the prediction residuals fall within a 15% error band. These results show that the proposed model can partially correct the prediction bias in traditional NEDC/CLTC standards under extreme operating conditions. It also provides a quantitative basis for driving decisions under complex conditions and may improve users' confidence in trip planning.