Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed related derivations. In this paper, we propose a novel approach to develop for heavy-duty vehicles. By implementing decision tree regression (DTR) the Fleet DNA on-road data, broader set of metrics, such as engine power fuel consumption, can be used more robust cycle development. Additionally, influence each metric target is also accounted by weighted number derived through DTR enhance representativenss cycle. As case studies, applied proposed method five vocations (drayage, long haul, regional local delivery, transit bus) most cycle, well four extreme (maximal energy maximal power-weighted work, fraction high speed, minimal economy) advance alternative powertrain design.