Citation

Hossen, Md Sabbir and Sarker, Md Tanjil and Al Qwaid, Marran and Ramasamy, Gobbi and Eng Eng, Ngu (2025) AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks. World Electric Vehicle Journal, 16 (7). p. 370. ISSN 2032-6653

Text wevj-16-00370-v2.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent.

Item Type:	Article
Uncontrolled Keywords:	smart grid; OCPP; load management; reinforcement learning; intrusion detection system; federated learning; cybersecurity; artificial intelligence; and electric vehicle charging networks
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 03:02
Last Modified:	27 Aug 2025 03:02
URII:	http://shdl.mmu.edu.my/id/eprint/14414

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