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.

Electric vehicles, Energy consumption, Machine learning, Physics-informed modeling.

1. Alamin, K. S. S., Chen, Y., Macii, E., Poncino, M., & Vinco, S. (2022). A machine learning-based digital twin for electric vehicle battery modeling. arXiv preprint. https://arxiv.org/abs/2206.08080 [Google Scholar] [Crossref]

2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 [Google Scholar] [Crossref]

3. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785–794. https://doi.org/10.1145/2939672.2939785 [Google Scholar] [Crossref]

4. Chen, Y., Sun, R., & Wu, X. (2021). Estimating bounds of aerodynamic, mass, and auxiliary load impacts on autonomous vehicles: A powertrain simulation approach. Sustainability, 13(22), 12405. https://doi.org/10.3390/su132212405 [Google Scholar] [Crossref]

5. Chen, Y., Wu, G., Sun, R., Dubey, A., Laszka, A., & Pugliese, P. (2021). A Review and Outlook on Energy Consumption Estimation Models for Electric Vehicles. SAE International Journal of Sustainable Transportation, Energy, Environment & Policy, 2(1), 79–96. https://doi.org/10.4271/13-02-01-0005 [Google Scholar] [Crossref]

6. Dataset source: EVspecs.org (2026), Most aerodynamic electric cars database. https://www.evspecs.org/most-aerodynamic-electric-cars [Google Scholar] [Crossref]

7. Di Martino, A., Miraftabzadeh, S. M., & Longo, M. (2022). Strategies for the modelisation of electric vehicle energy consumption: A review. Energies, 15(21), 8115. https://doi.org/10.3390/en15218115 [Google Scholar] [Crossref]

8. Fattahi, A., Sijm, J., & Faaij, A. (2020). A systemic approach to analyze integrated energy system modeling tools: A review of national models. Renewable and Sustainable Energy Reviews, 133, 110195. https://doi.org/10.1016/j.rser.2020.110195 [Google Scholar] [Crossref]

9. Guzzella, L., & Sciarretta, A. (2013). Vehicle propulsion systems: Introduction to modeling and optimization (3rd ed.). Springer. https://doi.org/10.1007/978-3-642-35913-2 [Google Scholar] [Crossref]

10. Hao, X., Wang, H., Lin, Z., & Ouyang, M. (2021). Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles. Journal of Cleaner Production, 278, 123415. https://doi.org/10.1016/j.jclepro.2019.119403 [Google Scholar] [Crossref]

11. Hussain, I., Ching, K. B., Uttraphan, C., Tay, K. G., & Noor, A. (2025). Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study. Scientific Reports, 15, 16124. https://doi.org/10.1038/s41598-025-94946-7 [Google Scholar] [Crossref]

12. Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009157926 [Google Scholar] [Crossref]

13. International Energy Agency. (2024). Global EV outlook 2024: Moving towards increased affordability. IEA. https://www.iea.org/reports/global-ev-outlook-2024 [Google Scholar] [Crossref]

14. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1 [Google Scholar] [Crossref]

15. Jiang, Z., Palacios, A., Zou, B., Zhao, Y., Deng, W., Zhang, X., & Ding, Y. (2022). A review on the fabrication methods for structurally stabilised composite phase change materials and their impacts on the properties of materials. Renewable and Sustainable Energy Reviews, 159, 112134. https://doi.org/10.1016/j.rser.2022.112134 [Google Scholar] [Crossref]

16. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html [Google Scholar] [Crossref]

17. Larminie, J., & Lowry, J. (2003). Electric vehicle technology explained. John Wiley & Sons. https://doi.org/10.1002/0470090707 [Google Scholar] [Crossref]

18. Latrach, A., Malki, M. L., Morales, M., Mehana, M., & Rabiei, M. (2023). A critical review of physics-informed machine learning applications in subsurface energy systems. https://arxiv.org/abs/2308.04457 [Google Scholar] [Crossref]

19. Lin, Y., Tang, J., Guo, J., Wu, S., & Li, Z. (2025). Advancing AI-enabled techniques in energy system modeling: A review of data-driven, mechanism-driven, and hybrid modeling approaches. Energies, 18(4), 845. https://doi.org/10.3390/en18040845 [Google Scholar] [Crossref]

20. Moawad, A., Gurumurthy, K. M., Verbas, O., et al. (2021). A deep learning approach for macroscopic energy consumption prediction with microscopic quality for electric vehicles. arXiv preprint. https://arxiv.org/abs/2111.12861 [Google Scholar] [Crossref]

21. Organisation for Economic Co-operation and Development. (2023). ITF Transport Outlook 2023. OECD Publishing. https://doi.org/10.1787/b6cc9ad5-en [Google Scholar] [Crossref]

22. Pokharel, S., Sah, P., & Ganta, D. (2021). Improved Prediction of Total Energy Consumption and Feature Analysis in Electric Vehicles Using Machine Learning and Shapley Additive Explanations Method. World Electric Vehicle Journal, 12(3), 94. https://doi.org/10.3390/wevj12030094 [Google Scholar] [Crossref]

23. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 [Google Scholar] [Crossref]

24. Ullah, I., Liu, K., Yamamoto, T., Al Mamlook, R. E., & Jamal, A. (2022). A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability. Energy & Environment, 33(8), 1583–1612. https://doi.org/10.1177/0958305X211044998 [Google Scholar] [Crossref]

25. Weiss, M., Cloos, K. C., & Helmers, E. (2020). Energy efficiency trade-offs in small to large electric vehicles. Environmental Sciences Europe, 32(1), 46. https://doi.org/10.1186/s12302-020-00307-8 [Google Scholar] [Crossref]

26. World Bank. (2023). The economics of e-mobility for passenger transportation. World Bank. https://www.worldbank.org/en/topic/transport/publication/the-economics-of-e-mobility-for-passenger-transportation [Google Scholar] [Crossref]

27. Zhang, X., Zhang, Z., Liu, Y., Xu, Z., & Qu, X. (2024). A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation. Renewable Energy, 234, 121243. https://doi.org/10.1016/j.renene.2024.121243 [Google Scholar] [Crossref]

• A Comparative Study on the Thermal and Electrical Conductivity of Common Materials
• Thickness Dependent Thermoelectric Properties of Pb0.4In0.6Se Thin Films Deposited by Physical Evaporation Technique
• Optimization of a Patch Antenna Using Genetic Algorithm
• Kinematic Constraints On Brown Dwarf Atmospheric Variability And Evidence For Bimodal Formation From Multi-Survey Analysis
• Reservoir Characterization through the Application of Petrophysical Evaluation of Well Logs of Animaux Field, Niger Delta Basin, Nigeria