Digital Twin-Based Simulation of Electric VehicleInteraction within Smart City Energy Networks

Abstract

The integration of electric vehicles (EVs) in smart city energy networks creates substantial challenges for managingenergy, stabilizing the grid, and optimizing infrastructure. For this reason, this paper studies both AI and IoT (in conjunctionwith digital twins) to manage wireless charging and Vehicle-to-Grid (V2G). We consider AI optimization methods that cansupport predictive energy management and real-time decision making, such as reinforcement learning, genetic algorithms, anddeep learning. The recommended framework includes edge-computing architecture, federated learning techniques, andMultiphysics models of EVs to improve grid stability and service quality. Our systematic review of the state of the art fordigital twins has revealed significant research gaps regarding integrating wireless power transfer and cybersecurity methods.Our simulation results indicate significant efficiency improvements with peak load reductions of 18%, improvement inforecasting accuracy of 29%, and reductions in state of charge error. In addition, we demonstrate 11%-15% reductions inenergy costs and an increase in battery lifespan of 8%-12%. Overall, this work provides a comprehensive roadmap fordeveloping intelligent, sustainable and resilient EV charging ecosystems that will resolve technical, economical, and ecologicalconsiderations while protecting the privacy of data and securing the system.