Dynamic Modeling and Optimization of Long-Haul EV Charging Networks

The transportation sector is an important contributor to greenhouse gas (GHG) emissions, with heavy-duty freight vehicles accounting for 31% of total transportation emissions in the United States. Electrifying long-haul freight vehicles presents a viable pathway to reduce emissions. Still, it has challenges such as limited high-power charging infrastructure, increased journey times, and regulatory constraints that hinder its widespread adoption. This study proposes a comprehensive optimization framework to address these challenges by integrating realistic charging station modeling, a mixed-integer linear programming (MILP) model for scheduling and multi-route optimization. The methodology leverages existing truck stop infrastructure, incorporates dynamic factors such as energy prices and station capacities, and adheres to operational constraints such as hours of service (HOS) regulations. The simulation results for three routes between Los Angeles, California, and San Antonio, Texas demonstrate trade-offs between cost, energy efficiency, and delivery performance, highlighting the importance of balancing these metrics. The findings underscore the critical role of advanced scheduling strategies in minimizing delays and costs and supporting the sustainable electrification of long-haul freight operations.