Citation

Hossen, Md Sabbir and Sarker, Md Tanjil and Ramasamy, Gobbi and Ngu, Eng Eng and Al Farid, Fahmid and Sadeque, Md. Golam (2025) Integration of AI-Driven Systems with Open Charge Point Protocol (OCPP) for Enhanced Electric Vehicle Charging Management. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

Text 15.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The rapid expansion of Electric Vehicle (EV) adoption necessitates the development of intelligent, interoperable, and efficient charging infrastructure. The Open Charge Point Protocol (OCPP) has emerged as a universal standard for communication between EV charging stations and backend systems, enabling vendor-neutral integration. At the same time, Artificial Intelligence (AI) offers transformative potential for optimizing charging operations, including load forecasting, dynamic pricing, fault detection, and user behavior analytics. This review investigates the integration of AI-driven methodologies within OCPP-compliant frameworks, presenting a comprehensive taxonomy of AI techniques categorized by application domains such as forecasting, scheduling, diagnostics, and personalization. A comparative analysis of current research highlights key performance metrics, including Mean Absolute Error (MAE), Utilization Rate, and Energy Cost Reduction, and reveals a reliance on simulation-based evaluation with limited real-world validation. Unresolved challenges such as standardization gaps, scalability constraints, and data privacy concerns are discussed, alongside emerging solutions like Federated Learning, Edge-AI, and Explainable AI. The review concludes by outlining a roadmap for future research, emphasizing hybrid learning models, middleware integration, and real-world pilot deployments to advance the development of scalable, secure, and intelligent EV charging ecosystems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Artificial Intelligence
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 08:26
Last Modified:	19 Mar 2026 02:40
URII:	http://shdl.mmu.edu.my/id/eprint/15595

Downloads

Edit (login required)