Development of a short-duration drive cycle to represent long-term measured drive cycle data

Tim Laclair, Zhiming Gao, Joshua Fu, Jimmy Calcagno, Jeongran Yun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Quantifying the fuel savings and emissions reductions that can be achieved from truck fuel efficiency technologies for a fleet's specific usage allows the fleet to select a combination of technologies that will yield the greatest operational efficiency and profitability. An accurate characterization of usage for the fleet is critical for such an evaluation; however, short-term measured drive cycle data do not generally reflect overall usage very effectively. This paper presents a detailed analysis of vehicle usage in a commercial vehicle fleet and demonstrates the development of a short-duration synthetic drive cycle with measured drive cycle data collected over an extended period of time. The approach matched statistical measures of the vehicle speed with acceleration history and integrated measured grade data to develop a compressed drive cycle that accurately represents total usage. Drive cycle measurements obtained during a full year from six tractor trailers in normal operations in a less-than-truckload carrier were analyzed to develop a synthetic drive cycle. The vehicle mass was also estimated to account for the variation of loads that the fleet experienced. These drive cycle and mass data were analyzed with a tractive energy analysis to quantify the benefits in terms of fuel efficiency and reduced carbon dioxide emissions that can be achieved on Class 8 tractor trailers by using advanced efficiency technologies, either individually or in combination. Although differences exist between Class 8 tractor trailer fleets, this study provides valuable insight into the energy and emissions reduction potential that various technologies can bring in this important trucking application. The methodology employed for generating the synthetic drive cycle serves as a rigorous approach to develop an accurate usage characterization that can be used to effectively compress large quantities of drive cycle data.

Original languageEnglish
Pages (from-to)63-74
Number of pages12
JournalTransportation Research Record
Volume2428
DOIs
StatePublished - Dec 1 2014

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