Resource-oriented optimization of electric vehicle systems: A data-driven survey on charging infrastructure, scheduling, and fleet management

Driven by growing concerns over air quality and energy security, electric vehicles (EVs) have experienced rapid development and are reshaping global transportation systems and lifestyle patterns. Compared with traditional gasoline-powered vehicles, EVs offer significant advantages, including lower energy consumption, reduced emissions, and lower operating costs. However, several core challenges remain to be addressed: (i) charging station congestion and operational inefficiencies during peak hours, (ii) high charging costs under time-varying electricity pricing schemes, and (iii) conflicts between charging needs and passenger service requirements. To address these challenges, this paper presents a comprehensive review of resource-oriented optimization models and approaches proposed in the literature. The reviewed studies cover the entire life cycle of EV systems, including charging station deployment, charging scheduling strategies, and large-scale fleet management. In addition, we compare successful and challenging real-world EV deployment cases worldwide to highlight the practical necessity of tailoring optimization algorithms to local infrastructure conditions. Moreover, we discuss the broader implications of EV integration across multiple domains, such as human mobility, smart grid infrastructure, and environmental sustainability, and identify key opportunities and directions for future research.