Author(s)

Elena Romano¹, Mark Petersen¹, Ingrid Madsen^2*

Affiliation(s)

¹Department of Mechanical and Electrical Engineering, University of Southern Denmark, Denmark.

²Department of Mechanical and Manufacturing Engineering, Aalborg University, Denmark.

Corresponding Author

Ingrid Madsen

ABSTRACT

Accurate prediction and effective optimization of energy consumption are pivotal to the advancement of electric vehicle (EV) technologies. This paper presents a machine learning-based framework for modeling, predicting, and minimizing EV energy consumption under varying operational conditions. By integrating real-world driving data with advanced regression and classification models, the study achieves high-accuracy forecasts of energy usage and proposes dynamic optimization strategies for enhanced efficiency. Experimental evaluations demonstrate that the proposed methods can reduce energy waste by up to 15% compared to conventional strategies. These results underscore the potential of data-driven approaches in driving sustainable electric mobility.

KEYWORDS

Electric vehicles; Energy consumption; Machine learning; Predictive modeling; Optimization; Sustainable transportation

CITE THIS PAPER

Elena Romano, Mark Petersen, Ingrid Madsen. Machine learning-based energy consumption prediction and optimization for electric vehicles. Academic Journal of Earth Sciences. 2025, 3(1): 27-32. DOI: https://doi.org/10.61784/ajes3012.

REFERENCES

[1] Mousavinezhad S, Choi Y, Khorshidian N, et al. Air quality and health co-benefits of vehicle electrification and emission controls in the most populated United States urban hubs: Insights from New York, Los Angeles, Chicago, and Houston. Science of The Total Environment, 2024, 912, 169577.

[2] Wang J, Tan Y, Jiang B, et al. Dynamic Marketing Uplift Modeling: A Symmetry-Preserving Framework Integrating Causal Forests with Deep Reinforcement Learning for Personalized Intervention Strategies. Symmetry, 2025, 17(4): 610.

[3] Tan Y, Wu B, Cao J, et al. LLaMA-UTP: Knowledge-Guided Expert Mixture for Analyzing Uncertain Tax Positions. IEEE Access, 2025, 13, 90637-90650. DOI: 10.1109/ACCESS.2025.3571502.

[4] Ahmad T, Madonski R, Zhang D, et al. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 2022, 160, 112128.

[5] Varga B O, Sagoian A, Mariasiu F. Prediction of electric vehicle range: A comprehensive review of current issues and challenges. Energies, 2019, 12(5): 946.

[6] Yang D, Liu H, Li M, et al. Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions. Journal of energy storage, 2023, 72, 108590.

[7] Automobiles F C, Belingardi G, Misul D, et al. Artificial Intelligence for Vehicle Engine Classification and Vibroacoustic Diagnostics. DeepTech Lab at Michigan State University and Fiat Chrysler Automobiles. 2020.

[8] Jin J, Xing S, Ji E, et al. XGate: Explainable Reinforcement Learning for Transparent and Trustworthy API Traffic Management in IoT Sensor Networks. Sensors (Basel, Switzerland), 2025, 25(7): 2183.

[9] Mousaei A, Naderi Y, Bayram I S. Advancing state of charge management in electric vehicles with machine learning: A technological review. IEEE Access, 2024, 12, 43255-43283.

[10] Kuutti S, Bowden R, Jin Y, et al. A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(2): 712-733.

[11] Kachirayil F, Weinand J M, Scheller F, et al. Reviewing local and integrated energy system models: insights into flexibility and robustness challenges. Applied energy, 2022, 324, 119666.

[12] Ahmed S F, Alam M S B, Hassan M, et al. Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artificial Intelligence Review, 2023, 56(11): 13521-13617.

[13] Wang J, Zhang H, Wu B, et al. Symmetry-Guided Electric Vehicles Energy Consumption Optimization Based on Driver Behavior and Environmental Factors: A Reinforcement Learning Approach. Symmetry, 2025, 17(6): 930.

[14] Forootan M M, Larki I, Zahedi R, et al. Machine learning and deep learning in energy systems: A review. Sustainability, 2022, 14(8): 4832.

[15] Alabi T M, Aghimien E I, Agbajor F D, et al. A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. Renewable Energy, 2022, 194, 822-849.

[16] Krzywanski J, Sosnowski M, Grabowska K, et al. Advanced computational methods for modeling, prediction and optimization—a review. Materials, 2024, 17(14): 3521.

[17] Recalde A, Cajo R, Velasquez W, et al. Machine learning and optimization in energy management systems for plug-in hybrid electric vehicles: a comprehensive review. Energies, 2024, 17(13): 3059.

[18] Rêgo A. Quo vadis? Insights into the determinants of evolutionary dynamics. Department of Zoology, Stockholm University, Sweden. 2023.

[19] Thomas P, Shanmugam P K. A review on mathematical models of electric vehicle for energy management and grid integration studies. Journal of Energy Storage, 2022, 55, 105468.

[20] Grabowski L, Drozd A, Karabela M M, et al. Aerodynamic and rolling resistances of heavy duty vehicles. Simulation of energy consumption. Applied Computer Science, 2024, 20(3): 116-131.

[21] Al-Wreikat Y, Serrano C, Sodré J R. Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving. Applied Energy, 2021, 297, 117096.

[22] Bandur V, Selim G, Pantelic V, et al. Making the case for centralized automotive E/E architectures. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1230-1245.

[23] Mozaffari S, Al-Jarrah O Y, Dianati M, et al. Deep learning-based vehicle behavior prediction for autonomous driving applications: A review. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(1): 33-47.

[24] Zhu Q, Huang Y, Lee C F, et al. Predicting electric vehicle energy consumption from field data using machine learning. IEEE Transactions on Transportation Electrification, 2024, 11(1): 2120-2132. DOI: 10.1109/TTE.2024.3416532.

[25] Hamza M H, Chattopadhyay A. Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response. Composite Structures, 2024, 345, 118360.

[26] Javed H, Eid F, El-Sappagh S, et al. Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches. Computing, 2025, 107(6): 1-64.

[27] Michailidis P, Michailidis I, Kosmatopoulos E. Reinforcement learning for optimizing renewable energy utilization in buildings: A review on applications and innovations. Energies, 2025, 18(7): 1724.

[28] Daryanavard S. Real-time predictive artificial intelligence: deep reinforcement learning for closed-loop control systems and open-loop signal processing. University of Glasgow, UK. 2024.

[29] Zhang Q, Chen S, Liu W. Balanced Knowledge Transfer in MTTL-ClinicalBERT: A Symmetrical Multi-Task Learning Framework for Clinical Text Classification. Symmetry, 2025, 17(6): 823.

[30] Fang Z. Adaptive QoS‐Aware Cloud–Edge Collaborative Architecture for Real‐Time Smart Water Service Management. Preprints 2025. DOI: https://doi.org/10.20944/preprints202505.2357.v1.  https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=4EAKq-oAAAAJ&citation_for_view=4EAKq-oAAAAJ:u5HHmVD_uO8C

[31] Rudin C, Chen C, Chen Z, et al. Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistic Surveys, 2022, 16, 1-85.

[32] Buhrmester V, Münch D, Arens M. Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction, 2021, 3(4): 966-989.

[33] Perez-Cerrolaza J, Abella J, Borg M, et al. Artificial intelligence for safety-critical systems in industrial and transportation domains: A survey. ACM Computing Surveys, 2024, 56(7): 1-40.

[34] Liu Y, Guo L, Hu X, et al. Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks. Sensors, 2025, 25(11): 3320.

[35] de la Iglesia D H, Corbacho C C, Dib J Z, et al. Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review. Batteries, 2025, 11(1): 17.

[36] Chen S, Liu Y, Zhang Q, et al. Multi-Distance Spatial-Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions. Advanced Intelligent Systems, 2025, 2400898. DOI: https://doi.org/10.1002/aisy.202400898.