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

以前，开发有代表性的驾驶循环的研究人员主要集中在轻型车辆上，只考虑车速和相关推导。在本文中，我们提出了一种开发重型车辆代表循环的新颖方法。通过对车队DNA公路车辆数据实施决策树回归（DTR），可以将更广泛的度量标准（例如发动机功率和燃料消耗）用于更稳健的循环开发。另外，每个指标对回归目标的影响也可以通过DTR得出的加权数来说明，以增强发达周期的代表性。作为案例研究，我们将建议的方法应用于五种重型职业（运输，长途运输，区域运输，本地送货和公交），并得出了最具代表性的周期，