Intelligent electric vehicle charging infrastructure: A comprehensive review of optimization, control, and grid integration strategies for sustainable mobility

The rapid proliferation of electric vehicles (EVs) has transformed power distribution networks, making the development and optimal management of Electric Vehicle Charging Stations (EVCS) a critical concern. Integrating EVCS with Renewable Energy Sources (RES) not only reduces fossil-fuel dependency but also enhances grid sustainability and lowers operational costs. However, variability in RES generation and unpredictable user charging patterns complicate station management. Strategic siting of EVCS is vital to minimizing network losses, flattening load profiles, and improving power quality. In response, this paper offers a systematic review of recent research on EVCS planning and control, examining optimization techniques for station location and capacity, advanced planning and control models, and grid-interactive charging strategies. We assess intelligent economic charging approaches—including dynamic scheduling, prediction-based control, and real-time grid interaction—and demonstrate how multi-objective optimization frameworks can reconcile cost efficiency, stability, and energy efficiency. Drawing on the IEA's Global EV Outlook 2025 projection of over 40 million EVs by 2030 (20 % CAGR since 2020), we underscore the need for scalable, RES-integrated charging frameworks aligned with techno-economic and policy-driven targets. Finally, we identify emerging trends in machine learning and artificial intelligence applications for predictive control and chart future research directions to enhance EVCS performance and grid integration.