Modeling the effects of congestion on fuel economy for advanced power train vehicles

Fuel-speed curves (FSC) are used to account for the aggregate effects of congestion on fuel consumption in transportation scenario analysis. This paper presents plausible FSC for conventional internal combustion engine (ICE) vehicles and for advanced vehicles such as hybrid electric vehicles, fully electric vehicles (EVs), and fuel cell vehicles (FCVs) using a fuel consumption model with transient driving schedules and a set of 145 hypothetical vehicles. The FSC shapes show that advanced power train vehicles are expected to maintain fuel economy (FE) in congestion better than ICE vehicles, and FE can even improve for EV and FCV in freeway congestion. In order to implement these FSC for long-range scenario modeling, a bounded approach is presented which uses a single congestion sensitivity parameter. The results in this paper will assist analysis of the roles that vehicle technology and congestion mitigation can play in reducing fuel consumption and greenhouse gas emissions from motor vehicles.

[1]  Tony Markel,et al.  Simulated fuel economy and performance of advanced hybrid electric and plug-in hybrid electric vehicles using in-use travel profiles , 2010, 2010 IEEE Vehicle Power and Propulsion Conference.

[2]  Stacy Cagle Davis,et al.  Transportation Energy Data Book: Edition 26 , 2007 .

[3]  Marc Ross,et al.  Evaluation of energy consumption, emissions and cost of plug-in hybrid vehicles , 2009 .

[4]  M. K. Singh,et al.  Multi-path transportation futures study : vehicle characterization and scenario analyses. , 2009 .

[5]  Kanok Boriboonsomsin,et al.  Real-World Carbon Dioxide Impacts of Traffic Congestion , 2008 .

[6]  Phillip Sharer,et al.  Evaluation of fuel consumption potential of medium and heavy duty vehicles through modeling and simulation. , 2010 .

[7]  Matthew Barth,et al.  Estimating Emissions and Fuel Consumption for Different Levels of Freeway Congestion , 1999 .

[8]  Anant D Vyas,et al.  VISION Model : description of model used to estimate the impact of highway vehicle technologies and fuels on energy use and carbon emissions to 2050. , 2004 .

[9]  Y. C. Chan,et al.  Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow? , 2008, Environ. Model. Softw..

[10]  D. Niemeier,et al.  How Much Can Vehicle Emissions Be Reduced?: Exploratory Analysis of an Upper Boundary Using an Emissions-Optimized Trip Assignment , 2002 .

[11]  Miguel A. Figliozzi,et al.  Analysis of the Relative Efficiency of Freeway Congestion Mitigation as an Emission Reduction Strategy , 2011 .

[12]  Zissis Samaras,et al.  Experimental evaluation of hybrid vehicle fuel economy and pollutant emissions over real-world simulation driving cycles , 2008 .

[13]  F Orecchini,et al.  A driving cycle for electrically-driven vehicles in Rome , 2003 .

[14]  Tony Markel,et al.  ADVISOR: A SYSTEMS ANALYSIS TOOL FOR ADVANCED VEHICLE MODELING , 2002 .

[15]  Stacy Cagle Davis,et al.  Transportation energy data book , 2008 .

[16]  Lei Yu,et al.  Estimation of Fuel Efficiency of Road Traffic by Characterization of Vehicle-Specific Power and Speed Based on Floating Car Data , 2009 .

[17]  R. Farrington,et al.  IMPACT OF VEHICLE AIR-CONDITIONING ON FUEL ECONOMY. TAILPIPE EMISSIONS, AND ELECTRIC VEHICLE RANGE: PREPRINT , 2000 .

[18]  P G Boulter,et al.  Emission factors 2009: Report 2 - a review of the average-speed approach for estimating hot exhaust emissions , 2009 .

[19]  Constantine Samaras,et al.  Life cycle assessment of greenhouse gas emissions from plug-in hybrid vehicles: implications for policy. , 2008, Environmental science & technology.