Co-optimization of charging scheduling and platooning for long-haul electric freight vehicles

Abstract

Freight mileages have been significantly increasing over the past decade, accounting for 11% of global greenhouse gas (GHG) emissions. Freight electrification and platooning are two promising technologies to mitigate the energy and environmental impacts of freight transportation. Effective coordination of charging and platooning are urgently needed to maximize the benefits of these two technologies, especially considering the current inadequate charging infrastructure, long charging duration, higher energy usage, and tight delivery schedules for long-haul electric freight vehicles (EFVs). In this study, we aim to co-optimize the platooning and charging strategies of EFVs. To achieve this goal, we developed a mixed-integer linear programming model to minimize the total system costs, including en-route charging cost, delivery delay cost, and hub charging cost. The proposed model was tested using a case study of a 595-mile Florida interstate highway route and solved by the state-of-the-art branch-and-cut algorithms, through which we demonstrated the effectiveness of using our model to identify the optimal charging and platooning schedules and quantify the spatial distribution of charging demand and platoon energy savings. We also performed sensitivity analyses to better understand the impacts of some critical factors, including the EFV models, charging station (CS) capacity, number of CSs, charging speed, and departure-time windows.