Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment

This study aims to seek the optimal deployment of fast-charging stations concerning the traffic flow equilibrium and various realistic considerations to promote Electric Vehicles (EVs) widespread adoption. A bi-level optimization framework has been developed in which the upper level aims to minimize the total system cost (i.e., capital cost, travel cost, and environmental cost). Meanwhile, the lower level captures travellers’ routing behaviours with stochastic demands and driving range limitation. A meta-heuristic approach has been proposed, combining the Cross-Entropy Method and the Method of Successive Average to solve the problem. Finally, numerical studies are conducted to demonstrate the proposed framework’s performance and provide insights into the impact of uncertain driving range and charging congestion on the planning decision and the system performance. Generally, both on-route congestion and charging congestion tend to be more serious when there are more EVs in the network; however, the system performance can be improved by increasing EVs’ driving range limitation and providing appropriate charging infrastructure. •Deployment of a bi-level optimization framework for fast-charging facility.•Minimization of capital, network congestion and the environmental cost.•Consideration of users’ route choice under stochastic demand and driving range.•Cross-Entropy-Method-based algorithm proposed.