Predictive Energy Management of Mild-Hybrid Truck Platoon Using Agent-Based Multi-Objective Optimization

The objective of this paper is to formulate and analyze the benefits of a predictive non-linear multi objective optimization method for a platoon of mild-hybrid line haul trucks. In this study a group of three trucks with hybrid electric powertrain are considered in a platoon formation where each truck has a predictive optimal control to save fuel with out any loss of trip time. While the controller on each truck uses the look ahead knowledge of the entire route in terms of road grade, the overall platoon controller used a multi agent method (Metropolis algorithm) to define coordination between the trucks. While the individual trucks, showed significant improvement in fuel economy when running on predictive mode, the true savings came from the entire platoon and showed promising results in terms of absolute fuel economy without trading off on total trip time. The proposed algorithm also proved to be significantly emission efficient. A platoon of 3 trucks achieved an average of 10% fuel savings while cutting back 13% on engine out NOx emissions for engine off coasting and 9.3% fuel saving with 8% emissions reduction for engine idle coast configuration when compared to non-predictive non-platoon configuration.