Abstract: The rapid penetration of electric vehicles (EVs) presents significant challenges and opportunities for modern smart grids. Uncoordinated EV charging can lead to increased peak demand, voltage deviations, and accelerated aging of distribution infrastructure, whereas coordinated charge scheduling can enhance grid reliability, reduce operational costs, and facilitate renewable energy integration. This paper presents a comprehensive review of EV charge scheduling strategies within smart grid environments. Various control architectures, including centralized, decentralized, and hierarchical approaches, are examined along with their corresponding optimization objectives such as cost minimization, peak load reduction, loss mitigation, and user comfort maximization. The review covers mathematical formulations based on deterministic, stochastic, and robust optimization, as well as emerging data-driven and reinforcement learning-based techniques for real-time scheduling under uncertainty. Bidirectional vehicle-to-grid (V2G) operations and their role in providing ancillary services are also discussed. Furthermore, commonly used datasets, simulation tools, and performance metrics for evaluating charging strategies are summarized. Finally, key challenges related to scalability, user behavior modeling, uncertainty management, and cyber security are highlighted, and future research directions toward intelligent, flexible, and user-centric EV charging frameworks are identified.

Keywords: Electric Vehicles (EVs), Smart Grid, Charge Scheduling, Smart Charging, Vehicle-to-Grid (V2G), Optimization Techniques, Reinforcement Learning, Demand Response, Renewable Energy Integration, Distribution Networks.

Downloads: | DOI: 10.17148/IARJSET.2025.121235