Description of Invention
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. It 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, including 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.