Machine Learning for Intelligent EV Charging Slot Booking During Low-Cost Grid Periods

Electric vehicles (EVs) are becoming increasingly popular worldwide due to their acknowledged environmental advantages, which coincide with global efforts to mitigate climate change. The popularity of pollution-free, affordable maintenance, government incentives, and technological developments in charging, drive customers to increase the use of electric vehicles (EVs). On distribution systems, however, the spike in power demands brought on by EV charging presents problems like excessive voltage swings, power outages, and possible overloads. It becomes necessary to have a strong power system management system that can protect the distribution grid from negative impacts by controlling EV charging intelligently to lessen these problems.This necessity led to the development of an inventive EV charging management system that optimizes EV routing to charging stations by utilizing machine learning (ML) techniques. The main objectives are minimizing load variance, minimizing power losses, stabilizing voltage swings, and cutting charging costs. This ML-driven strategy makes strategic management of EV charging following minimum grid loads possible, based on analyzing previous data to create accurate predictions about future grid circumstances. Additionally, the system incorporates an easy-to-use website that offers EV owners a handy interface to reserve charging places based on anticipated minimum loads. By providing real-time information, predictive slot booking features, customizable preferences, and seamless payment integration, this website improves user experience. It offers a comprehensive solution that prioritizes user convenience in the ever-evolving field of sustainable mobility, while also addressing grid management challenges and reducing the charging cost.