Adaptive Energy Management System for Electric Vehicle Charging Stations: Leveraging AI for Real-Time Grid Stabilization and Efficiency

¹ Associate Professor, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait.
² Institute of Business Management, GLA University, Mathura, kushagra.kulshrestha@gla.ac.in
³ Department of Computer Engineering,Vishwakarma Institute of Technology Pune India aarti.agarkar@vit.edu
⁴ Assistant Professor,Department of CIVIL,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127.,k.karthika_civil@psvpec.in
⁵ Asst Professor,Department of EEE,New Prince Shri Bhavani College of Engineering and Technology Chennai - 600073, Tamil nadu,India.sarathy@newprinceshribhavani.com
⁶ Bharati Vidyapeeth’s college of Engineering for women, Pune, avinash.m.pawar@bharatividyapeeth.edu
⁷ Assistant Professor, Department of Electronics and Communication Engineering, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India

^* abostani@auk.edu.kw

Abstract

The increasing demand for electric vehicles (EVs) presents significant challenges for energy grids, particularly in balancing demand and supply during peak charging periods. This paper proposes an Adaptive Energy Management System (EMS) for EV charging stations that leverages artificial intelligence (AI) techniques to optimize power distribution and enhance grid stability. By integrating fuzzy logic and reinforcement learning algorithms, the proposed system dynamically adjusts charging power allocation based on real-time grid conditions and EV battery levels. The EMS ensures efficient energy use, minimizes grid overload risks, and enables seamless integration with renewable energy sources. Simulation results demonstrate the system’s ability to maintain grid stability while maximizing charging efficiency. This adaptive approach paves the way for future smart grid applications, offering scalability and robustness for large-scale EV deployments.