EV Charging Infrastructure Analytics and Demand Forecasting Using Machine Learning

Abstract

The rapid adoption of Electric Vehicles (EVs) has significantly increased the demand for efficient and scalable charging infrastructure. However, unplanned deployment of charging stations can lead to congestion, uneven distribution of resources, and inefficient energy utilization. This research proposes an EV Charging Infrastructure Analytics and Demand Forecasting system that uses data analytics and machine learning techniques to analyze EV charging patterns and predict future demand. The proposed system uses historical EV charging station data, traffic patterns, and geographical information to identify peak charging hours and high?demand locations. Machine learning algorithms including ARIMA, Random Forest, LSTM, and XGBoost are used to forecast charging demand with high accuracy. The results help policymakers, investors, and urban planners make data?driven decisions for EV infrastructure development. The proposed solution improves charging station placement, reduces waiting time for EV users, and supports sustainable transportation planning for smart cities.

Introduction

Electric vehicles (EVs) are key to reducing carbon emissions, but planning efficient charging infrastructure remains a challenge due to uneven station distribution and fluctuating demand. Existing methods rely on limited data and manual assumptions, leading to underused or overburdened stations.

The proposed system, an EV Charging Infrastructure Analytics and Demand Forecasting platform, uses machine learning models—ARIMA, Random Forest, LSTM, and XGBoost—on historical charging data, traffic patterns, and EV adoption trends to forecast demand and identify optimal charging station locations. The system includes modules for data collection, preprocessing, ML modeling, backend processing, and a user-friendly interface with dashboards.

Experimental results show XGBoost achieving 93.8% accuracy, outperforming other models. The system enables better infrastructure planning, reduces EV waiting times, supports investors, and promotes sustainable transportation.

Conclusion

This research presented an EV Charging Infrastructure Analytics and Demand Forecasting system using machine learning techniques. The system analyzes EV charging patterns and predicts future demand with high accuracy. The insights generated by the system help governments, investors, and urban planners plan charging infrastructure efficiently. This data?driven approach supports sustainable transportation and promotes faster adoption of electric vehicles.

References

[1] A. De Cauwer, J. Van Mierlo, “Electric Vehicle Energy Consumption Prediction Using Machine Learning.” [2] J. Wu, H. Zhang, “Machine Learning Based Energy Consumption Prediction of Electric Vehicles.” [3] S. Severson et al., “Prediction of Lithium?Ion Battery Health for Electric Vehicles,” Nature Energy. [4] Y. Wang, X. Ma, “Forecasting Electric Vehicle Charging Demand Using Time Series Models.” [5] R. Mehta, S. Jain, “Electric Vehicle Analytics Using Machine Learning Techniques.”

Copyright

Copyright © 2026 S. Haseena, S. Imamsharif, S. Fahath, S. Khajarasool, S. Afreed, Mrs. Kokila Subramanyam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.