Smart Charging Solutions for Electric Vehicles: An AI-Driven Approach to Load Balancing and Grid Integration

Abstract

Today, the integration of Electric Vehicles (EVs) into the grid is a critical issue with inefficiencies in real-time demand management, scalability, as well as security threats to centralized infrastructure. The fast increase in EV adoption poses several challenges including grid overloading and energy distribution wastage. Thus, there is a need for an intelligent, scalable, and secure charging solution to avert disruption as well as improve energy efficiency that will ensure the sustainable growth of electric mobility. The study emphasizes on development of an AI-driven platform using demand response and load-balancing techniques for EV charging. Including predictive analytics based on AI technology, the system enhances grid stability in addition to optimizing energy consumption. To resolve the challenges, the paper presents load balancing with an Artificial Intelligence (AI) system employing AI-driven predictive demand forecasting. Dynamic load balancing optimizes EV charging infrastructure. The system proposed in this study enhances the stability of the grid, with a 20% decrease in peak-period overload, as well as a cost reduction of 20.38%. These results offer efficient and sustainable EV charging infrastructure facilitating the broader deployment of electric mobility.