ARTIFICIAL INTELLIGENCE ROLE IN OPTIMIZING ELECTRIC VEHICLE CHARGING PATTERNS, REDUCE COSTS, AND IMPROVE OVERALL EFFICIENCY: A REVIEW

Abstract

The global popularity of electric cars (EVs) as a sustainable means of transportation, reliable and efficient charging infrastructure is essential. Traditional electric vehicle charging involves connecting the car to a power source and waiting for the battery to charge. However, AI has allowed us to improve charging patterns, reduce costs, and boost efficiency. This article examines how AI algorithms are changing electric car charging. If electric vehicle (EV) charging and discharging are not coordinated, the power supply infrastructure will be overrun. Demand response like dynamic pricing might encourage electric vehicle owners to participate in scheduling initiatives. Thus, EV charging and discharging scheduling and dynamic pricing model research are crucial. Artificial intelligence-based models for EV charging predictions and scheduling have been the focus of researchers. These models outperform linear, exponential, and multinomial logit optimization approaches. Due to the novelty and ongoing development of EVs returning electricity to the power grid, vehicle-to-grid (V2G) systems have received little attention. Thus, a complete analysis of EV charging and discharging research is needed to identify gaps and improve future studies. This study categorizes EV charging and discharging studies into forecasting, scheduling, and pricing techniques. The work links forecasting, scheduling, and pricing processes and identifies research gaps in EV discharge scheduling and dynamic pricing models.