The transition to zero-emission freight transportation is a critical component of California’s climate strategy, yet the adoption of battery electric trucks (BETs) in both long-haul and short-haul operations faces significant challenges. Limited charging infrastructure, long charging durations, grid reliability concerns, and regulatory constraints—such as Hours of Service (HOS) requirements—pose operational hurdles for fleet operators. This study develops a comprehensive optimization framework for electric truck fleet management, addressing the interplay between infrastructure limitations, operational uncertainties, and energy cost fluctuations. This research will also provide scalable insights for policymakers and industry leaders, supporting the broader transition to sustainable freight transportation. Investments in ultra-fast charging infrastructure, extended-range BETs, and microgrid-based energy management are key to accelerating the electrification of freight operations while ensuring cost efficiency and operational resilience. For long-haul electric trucking, this study leverages real-world data to optimize charging stops, minimize delays, and ensure regulatory compliance. Using a combination of mixed-integer programming and linearization techniques, the proposed model dynamically identifies cost-effective charging strategies, balancing factors such as driver wages, energy costs, and charging delays. For short-haul trucking, this study introduces a novel dispatching model—the electric vehicle routing problem with backhauls and time windows under uncertainty.
