A Grid-Aware Two-Stage Dynamic Routing and Charging Station Selection Framework for Electric Vehicles Under Traffic–Energy Coordination

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure.