Coordinating Truck Platooning by Clustering Pairwise Fuel-Optimal Plans

TL;DR

This paper presents a clustering-based optimization method for coordinating truck platooning to maximize fuel savings, demonstrating its effectiveness through simulations involving thousands of trucks.

Contribution

It introduces a novel clustering algorithm for large-scale truck platooning coordination based on pairwise fuel-optimal plans, with proven convergence and scalability.

Findings

The algorithm efficiently computes plans for thousands of trucks.

Significant fuel savings are achievable with the proposed method.

The approach is validated through Monte Carlo simulations.

Abstract

We consider the fuel-optimal coordination of trucks into platoons. Truck platooning is a promising technology that enables trucks to save significant amounts of fuel by driving close together and thus reducing air drag. We study how fuel-optimal speed profiles for platooning can be computed. A first-order fuel model is considered and pairwise optimal plans are derived. We formulate an optimization problem that combines these pairwise plans into an overall plan for a large number of trucks. The problem resembles a medoids clustering problem. We propose an approximation algorithm similar to the partitioning around medoids algorithm and discuss its convergence. The method is evaluated with Monte Carlo simulations. We demonstrate that the proposed algorithm can compute a plan for thousands of trucks and that significant fuel savings can be achieved.