Development of Heavy-Duty Vehicle Representative Driving Cycles via Decision Tree Regression

Chen Zhang, Andrew Kotz, Kenneth Kelly, Luke Rippelmeyer

Research output: Contribution to journalArticlepeer-review

12 Scopus Citations


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.

Original languageAmerican English
Article numberArticle No. 102843
Number of pages28
JournalTransportation Research Part D: Transport and Environment
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors

NREL Publication Number

  • NREL/JA-5400-78876


  • Decision tree regression
  • Heavy-duty vehicle
  • On-road data
  • Powertrain electrification
  • Representative driving cycle


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