Fuel Efficient High-Density Platooning Using Future Conditions Prediction

Abstract

A promising application of cooperative driving is high-density platooning, which main goal is to reduce fuel consumption by driving with inter-vehicle distances below ten meters. The prediction of factors influencing the platoon capability to drive with such inter-vehicle distances the derived safe inter-vehicle distances, drives the potential fuel saving. Our aim is to study the influence of the prediction, especially the prediction horizon, on the achieved fuel saving as a function of different maneuver parameters. The contributions of this paper are: introducing the concept of maneuver reference to distribute the effort of maneuvering in truck platooning; linking the fuel consumption to a compensation time, that is the time during which the platoon will counterbalance the fuel consumption by benefiting from the reduced air drag; presenting an optimization method for maximizing the fuel saving depending on some predictive quality of service parameters. To model the fuel consumption and the duration of the maneuvers, we use a lasso regressions on data obtained from simulation. We then use these regression models in our optimization framework, which is based on particle swarm optimization. We show that to benefit from high-density platooning, the magnitude order of the prediction horizon required by a five-truck platoon is minimum hundred seconds.