Coordinating Truck Platooning by Clustering Pairwise Fuel-Optimal Plans
For fleet operators, this work provides a scalable method to coordinate platooning, though it is incremental as it adapts existing clustering techniques.
The paper tackles fuel-optimal coordination of trucks into platoons by clustering pairwise optimal plans. The proposed algorithm computes plans for thousands of trucks, achieving significant fuel savings.
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.