Electric Truck Platooning with Charging Consideration and Leader Swapping

TL;DR

This paper develops a comprehensive optimization framework for electric truck platooning on road networks, considering routing, charging, platoon formation, and leader swapping to reduce costs and improve operational efficiency.

Contribution

It introduces a MILP model and an ALNS algorithm for joint optimization of routing, charging, platooning, and leader swapping in electric truck operations on networks.

Findings

Platooning reduces operational costs by up to 2.77%.

The ALNS algorithm significantly speeds up computation, solving large instances in about 120 seconds.

Network-wide optimization improves electric truck deployment efficiency.

Abstract

Electric trucks are increasingly deployed to reduce the trucking sector's carbon footprint, but their limited range and charging needs create operational challenges on mid- to long-haul routes. Truck platooning can mitigate range anxiety through energy savings and, in turn, influence routing and charging decisions, yet most existing studies focus on a single highway corridor and do not capture network-wide operations. We study electric truck platooning on a general road network, where trucks must select routes and charging stations with heterogeneous prices and charging speeds, form platoons on shared arcs, and possibly take detours that trade off platoon savings with additional labor hours. We further allow in-platoon position swaps so that leading responsibility rotates, balancing battery usage and avoiding early depletion of any single truck. To jointly optimize routing paths, charging-station choices, labor time, and platoon formation and position swaps, we formulate a mixed-integer linear program (MILP). Because exact methods become intractable on realistic instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm enhanced with a savings-based bounding scheme, infeasible-pair elimination, and candidate-station filtering. Computational experiments on test instances with up to 150 trucks show that incorporating platooning can reduce total operational costs by up to 2.77 percent, while the proposed algorithm cuts computation time by up to 99.96 percent compared with CPLEX and solves 150-truck instances in about 120 seconds, indicating strong potential for real-world applications.