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Electric Truck Platooning with Charging Consideration and Leader Swapping

Yilang Hao, Zhibin Chen

TL;DR

The paper addresses joint routing, charging, and platooning for electric trucks on a road network, incorporating heterogeneous charging prices and labor costs. It formulates a MILP that jointly optimizes routing, charging, platoon formation, and leader swapping, and tackles scalability with an Adaptive Large Neighborhood Search (ALNS) enhanced by pre-processing and diverse operators. Empirical results show up to 2.77% total cost savings from platooning on large networks and that ALNS scales to 150 trucks in about 120 seconds, far outperforming exact solvers in large instances. The work highlights the practical value of network-wide platooning and leader rotation for energy efficiency and cost reduction in mid- to long-haul electrified freight operations.

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.

Electric Truck Platooning with Charging Consideration and Leader Swapping

TL;DR

The paper addresses joint routing, charging, and platooning for electric trucks on a road network, incorporating heterogeneous charging prices and labor costs. It formulates a MILP that jointly optimizes routing, charging, platoon formation, and leader swapping, and tackles scalability with an Adaptive Large Neighborhood Search (ALNS) enhanced by pre-processing and diverse operators. Empirical results show up to 2.77% total cost savings from platooning on large networks and that ALNS scales to 150 trucks in about 120 seconds, far outperforming exact solvers in large instances. The work highlights the practical value of network-wide platooning and leader rotation for energy efficiency and cost reduction in mid- to long-haul electrified freight operations.

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.

Paper Structure

This paper contains 30 sections, 15 equations, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature
  3. Problem statement
  4. Illustrative example
  5. Mathematical formulation
  6. Objective function
  7. Truck flow constraints
  8. SOC constraints
  9. Time consistency constraints
  10. Platoon constraints
  11. Integral constraints
  12. Solution Methodology
  13. Pre-processing
  14. Candidate charging station decision
  15. Initial Solution
  16. ...and 15 more sections

Figures (8)

  • Figure 1: Illustrative example
  • Figure 2: Flow chart of solution algorithm
  • Figure 3: Small test network
  • Figure 4: Operation comparisons of 3 different scenarios
  • Figure 5: Yangtze River Delta network delta2022
  • ...and 3 more figures