Citation

Hossen, Md Sabbir and Sarker, Md Tanjil and Nabi, Md Serajun and Bannah, Hasanul and Ramasamy, Gobbi and Ngu, Eng Eng (2025) Federated AI-OCPP Framework for Secure and Scalable EV Charging in Smart Cities. Urban Science, 9 (9). p. 363. ISSN 2413-8851

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Abstract

The rapid adoption of electric vehicles (EVs) has intensified the demand for intelligent, scalable, and interoperable charging infrastructure. Traditional EV charging networks based on the Open Charge Point Protocol (OCPP) face challenges related to dynamic load management, cybersecurity, and efficient integration with renewable energy sources. This paper presents a novel AI-driven framework that integrates federated learning, predictive analytics, and real-time control within OCPP-compliant networks to enhance performance and sustainability. The proposed system utilizes edge AI modules at charging stations, supported by a central aggregator that employs federated learning to preserve data privacy while enabling network-wide optimization. A case study involving simulated smart charging stations demonstrates significant improvements, including an 18% reduction in peak load demand, a 29% increase in forecasting accuracy (MAPE of 8.5%), a 10% decrease in average charging wait times, and a 12% increase in on-site solar energy utilization. The framework’s compatibility with OCPP and related standards (e.g., IEC 61851, ISO 15118) ensures ease of deployment on existing infrastructure. These results indicate that the proposed AI-OCPP integration provides a scalable and intelligent foundation for next-generation EV charging networks that align with the goals of sustainable transportation and smart grid evolution.

Item Type:	Article
Uncontrolled Keywords:	Electric vehicles
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK301-399 Electric meters
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	07 Nov 2025 06:13
Last Modified:	07 Nov 2025 06:13
URII:	http://shdl.mmu.edu.my/id/eprint/14768

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