Authors: Nighot Onkar Kailas, Professor Bangar Abhishek Popat, Professor S. B. Bhosale, Dr. A.A.Khatri
Abstract: The rapid growth of electric vehicles (EVs) concentrates charging demand in time and space, which can overload individual charging stations and local grid assets while neighbouring stations remain underused. Fixed-rule or first-come-first-served control cannot adapt well to uncertain arrivals, prices and grid limits. Artificial intelligence (AI), combined with IoT metering and communication, is increasingly proposed for forecasting charging demand and for balancing load across charging stations. This paper presents a focused narrative review of studies that use machine learning (ML), deep learning (DL) and reinforce-ment learning (RL) for EV charging load forecasting, scheduling and load balancing. It examines three connected themes: data sensing, learning methods, and IoT architectures. The reviewed literature reports LSTM-based short-term load forecasting on Adaptive Charging Network data with a mean absolute error of 4 kW, a graph-based RL approach that balances load across a station network through dynamic pricing in simulation, batch RL that coordinates several stations using real-world charging data, and surveys that classify RL methods into centralized and decentralized categories. However, most studies are simulation-based, single-dataset or single-campus, and few report field validation, communication failure handling, or closed-loop opera-tion on physical chargers. The paper contributes a comparative table of representative studies, a conceptual data-to-decision architecture (not experimentally validated), and a prioritised list of research gaps.
