Optimizing Electric Vehicle Charging Infrastructure on Highways: A MILP Model for Balanced Demand Allocation

The strategic placement and sizing of electric vehicle (EV) charging stations on highways are critical for alleviating range anxiety and fostering widespread EV adoption. This paper presents a novel mixed‐integer linear programming (MILP) model for optimizing the location and capacity of charging stations along highway corridors. Unlike traditional approaches, our formulation explicitly models the distribution of charging demand from each origin–destination (O/D) pair among the multiple stations along its path. The nonlinearities inherent in this flow‐sharing mechanism are efficiently handled via a piecewise linear approximation, ensuring model tractability for real‐world instances. Using real geographical and demographic data, we generate realistic case studies for two U.S. highways, I–70 and I–95. Our analysis evaluates the trade‐offs between cost, service quality, and infrastructure layout for different charger power levels (50, 150, and 350 kW) and EV adoption scenarios. Results indicate that 350 kW chargers generally offer the most cost‐effective solution while significantly reducing expected user times by up to 70% compared to 50 kW chargers. The model provides a practical decision‐support tool for planners, balancing computational efficiency with a high‐fidelity representation of network‐wide charging dynamics.