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Advanced Energy Management System for Electric Vehicle Charging Stations Using Machine Learning Algorithms
1 2 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 3 Associate Professor and HOD, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India.
Published Online: May-August 2025
Pages: 11-14
The rapid adoption of electric vehicles (EVs) is transforming the transportation sector and increasing the demand for efficient charging infrastructure. As the number of EVs continues to grow, charging stations face significant challenges related to power demand fluctuations, grid stability, energy costs, and charging efficiency. Conventional charging management systems often operate using static scheduling approaches that are unable to respond effectively to dynamic charging requirements and variable electricity prices. Consequently, intelligent energy management solutions are required to optimize charging operations while maintaining grid reliability. This study proposes an advanced energy management system for electric vehicle charging stations utilizing machine learning algorithms. The proposed framework integrates smart sensors, real-time monitoring technologies, renewable energy sources, battery energy storage systems, and intelligent decision-making models. Machine learning techniques are employed to predict charging demand, optimize energy allocation, reduce peak load conditions, and improve station utilization. Historical charging data, weather conditions, user behavior, and electricity pricing information are analyzed to support adaptive charging strategies. The proposed system enables efficient coordination between charging infrastructure and electrical grids while maximizing renewable energy utilization. Performance evaluation demonstrates significant improvements in energy efficiency, charging station utilization, operational cost reduction, and peak demand management. The findings indicate that machine learning-based energy management systems can play a crucial role in supporting sustainable transportation and smart city development. The framework provides a scalable and intelligent solution for future EV charging networks and next-generation energy ecosystems.
