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LONG_HAUL TRUCKING: ROUTING TRUCKS UNDER THE CONSTRAINTS OF PARKING AVAILABILITY, CHARGING STATION LOCATIONS, CHARGING TIMES, AND HOURS OF OPERATION & RECONFIGURATION OF ROUTES TO ACCOMMODATE GRID SERVICE DISRUPTION - This study explores optimizing electric truck fleet management under limited and uncertain charging infrastructure. It focuses on the Long Beach to San Francisco freight route. The study develops a dynamic optimization framework that combines mixed-integer programming with the linearization of nonlinear battery state of charge (SOC) constraints. The model integrates key variables, such as SOC, charging station locations, traffic conditions, and driver rest periods. The goal is to minimize operational costs while ensuring compliance with Hours of Service (HOS) regulations.
The findings reveal significant cost-saving opportunities. Ultra-fast chargers (1000 kW) can reduce operational costs by up to 25%. Extended-range trucks (600-mile configurations) further reduce costs by 10% due to fewer charging stops. Electric trucks show a 20% cost advantage over diesel trucks under baseline electricity prices. However, at higher energy prices, 300-mile-range trucks experience cost convergence with diesel trucks. By addressing nonlinear SOC constraints, the model adapts well to dynamic conditions. For example, it successfully reconfigures routes during grid disruptions, resulting in only a 1.5% increase in costs.
This study highlights key priorities for future investments. Ultra-fast charging stations, extended-range vehicles, and backup charging networks are essential for sustainable long-haul electric freight. The proposed framework offers practical, scalable solutions for policymakers and industry leaders seeking to promote cost-effective, sustainable freight transport.
