Network-level optimal coordination of charging and platooning for electric trucks

Purpose Electric trucks and platooning are promising technologies to reduce greenhouse gas emissions in the freight sector. To maximize the benefits of these two technologies, effective coordination of charging and platooning is essential, especially considering insufficient charging stations (CSs), long charging duration and tight freight delivery window for middle-mile electric trucks. Therefore, this paper aims to jointly optimize the scheduling of charging and platooning of electric trucks over the freight transportation network. Design/methodology/approach This paper proposes a mixed integer linear programming model to minimize the total costs from en-route charging, depot charging, and delivery delay. This also presents scenario analyses to understand the impacts of key features on system costs, including battery capacity, number of charging plugs at CSs, charging speed, availability of alternative paths and platoon energy-saving percentage. To solve the model with a large fleet size, a warm-start-based parameter-tuned solver approach, and hybrid metaheuristics of variable neighborhood search and local branching were implemented and compared based on performance. Findings The proposed model was implemented using the freight network in Florida. In a case study with a small fleet size, platoon scheduling reduced 19% of en-route charging cost and 30% of delivery delay cost compared with the case of only charge scheduling. Electric trucks were charged around three times with an average duration of 35 min per session to facilitate platoon scheduling and minimize the total cost. Originality/value Previous models optimized charging and platoon scheduling for single routes that cannot be generalized for network level and multiple origin-destination pairs; this study addresses network-level optimization.