Look-ahead controls of heavy duty trucks on open roads — six benchmark solutions

Abstract

A benchmark problem for fuel efficient control of a truck on a given road profile has been formulated and solved. Six different solution strategies utilizing varying degrees of off-line and on-line computations are described and compared. A vehicle model is used to benchmark the solutions on different driving missions. The vehicle model was presented at the IFAC AAC 2016 symposium and is compiled from model components validated in previous research projects. The driving scenario is provided as a road slope profile and a desired trip time. The problem to solve is a combination of engine-, driveline-and vehicle-control while fulfilling demands on emissions, driving time, legislative speed, and engine protections. The strength of this publication is the collection of all six different solutions in one paper. This paper is intended to provide a starting point for practicing engineers or researchers who work with optimal and/or model based vehicle control.

The Electrochemical Commercial Vehicle (ECCV) Platform

“…The motion-dependent forces that act on the chassis are the rolling, air drag, and gravitational resistance, of which the gravitational resistance is the dominant term in a heavy-duty truck. The model parameters for the chassis and gearbox are taken from a previous truck benchmark problem [24].…”

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

“…All considered techniques are non-causal, except the ECMS-based one, which uses current and past information only. Both DP [13][14][15][16][17] and PMP [18,19] techniques are well established and have been extensively presented in the literature for hybrid powertrain optimization, and are here therefore used to evaluate the performance of the newly proposed FADP and ECMS-based approaches. In the following, a brief overview on optimal control problem formulation is given, followed by the introduction of the bases of all the considered methods (both conventional and novel) and the analysis of their performance for hybrid powertrain optimization.…”

“…Further detail on Dynamic Programming can be found in [21,22], whereas its application to the hybrid vehicle problem can be found, e.g., in [2,13,[15][16][17][23][24][25][26].…”