A Strategic EV Charging Networks Planning Framework for Intercity Highway With Time-Expanded User Equilibrium

The rapid adoption of electric vehicles (EVs) has underscored the critical need for reliable highway charging infrastructure to support seamless long-distance travel within intercity transportation networks. However, conventional EV traffic flow modeling often neglects the interactions among users regarding charging decisions at origin cities versus along intercity highways. To address this gap, this study proposes a novel spatiotemporal path-based traffic assignment model incorporating a time-expanded user equilibrium (UE) mechanism. The model captures the interdependencies between charging and routing modes by accounting for EV users’ decisions on departure times, route selection, and charging options. Furthermore, a robust optimization framework is developed for the planning of highway charging networks, with the objective of maximizing the profits of private investors acting as policy takers. To address the challenges posed by binary decision variables in the inner-level of robust formulation, a novel no-good cut-based reformulation is applied to enhancing computational efficiency. The proposed framework is validated on a 31-node highway network in Jiangsu Province, China. Numerical results demonstrate its effectiveness in optimizing highway charging infrastructure and improving profitability for stakeholders.