Effects of using synthesized Driving Cycles on vehicle fuel consumption

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Abstract

Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly, improvements on the DC synthesis method are proposed, to reach a more realistic slope profile and more accurate fuel consumption and CO2 emission estimates. The effects of using synthesized DCs on fuel consumption are investigated considering three different vehicle models: conventional ICE, and full hybrid and mild hybrid electric vehicles. Results show that short but representative synthetic DCs will results in more realistic fuel consumption estimates (e.g. in the 5%-10% range) and in much faster simulations. Using the results of this proposed method also eliminates the need to use very simplified DCs, as the New European Driving Cycle(NEDC), or long, measured DCs.

Original languageEnglish
Pages (from-to)7505-7510
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - 1 Jul 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org/

Keywords

  • Driving cycle
  • Efficiency
  • Markov Chain
  • Monte Carlo
  • Powertrain Design

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