Optimization techniques for siting solar-powered EV charging stations: A systematic review and methodological classification

Abstract

Solar-powered electric vehicle (EV) charging stations are essential in advancing low-carbon transportation. However, determining optimal locations remains challenging due to spatial, technical, and environmental constraints. This systematic review, conducted under the PRISMA 2020 framework, synthesizes optimization techniques for siting solar-powered EV charging stations from 15 peer-reviewed studies published between 2016 and 2024. The reviewed methods are classified into five major categories: geographic information systems (GIS)-based spatial models, multi-criteria decision-making (MCDM) frameworks, hybrid approaches integrating fuzzy logic and GIS, heuristic/metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), and artificial-intelligence-based models for predictive site selection. GIS-MCDM hybrid approaches were the most prevalent, offering improved robustness in spatial decision-making. Nevertheless, the literature reveals persistent gaps, including limited empirical validation, insufficient use of real-time data, and weak integration with smart-grid planning. This review provides a structured methodological classification, highlights sustainability considerations, and outlines a research roadmap toward intelligent, data-driven, and sustainable EV infrastructure planning aligned with global energy-transition goals.