Transportation Energy Pathways LDRD.

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This report presents a system dynamics based model of the supply-demand interactions between the USlight-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year2050. An important capability of our model is the ability to conduct parametric analyses. Others have reliedupon scenario-based analysis, where one discrete set of values is assigned to the input variables and used togenerate one possible realization of the future. While these scenarios can be illustrative of dominant trendsand tradeoffs under certain circumstances, changes in input values or assumptions can have a significantimpact on results, especially when output metrics are associated with ... continued below

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46 p.

Creation Information

Barter, Garrett; Reichmuth, David; Westbrook, Jessica; Malczynski, Leonard A.; Yoshimura, Ann S.; Peterson, Meghan et al. September 1, 2012.

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This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this report can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories (SNL-CA), Livermore, CA (United States) and Albuquerque, NM
    Place of Publication: Albuquerque, New Mexico

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Description

This report presents a system dynamics based model of the supply-demand interactions between the USlight-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year2050. An important capability of our model is the ability to conduct parametric analyses. Others have reliedupon scenario-based analysis, where one discrete set of values is assigned to the input variables and used togenerate one possible realization of the future. While these scenarios can be illustrative of dominant trendsand tradeoffs under certain circumstances, changes in input values or assumptions can have a significantimpact on results, especially when output metrics are associated with projections far into the future. Thistype of uncertainty can be addressed by using a parametric study to examine a range of values for the inputvariables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors thatinfluence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction ofpetroleum consumption within the US LDV fleet. The underlying model emphasizes competition between13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technologicaldevelopment for the electric powertrain, battery performance, as well as the efficiency improvements inconventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. Theconsumer effective payback period, in particular, can significantly increase the market penetration rates ifextended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressiveGHG emission reduction targets, even as the current electricity source mix shifts away from coal and towardsnatural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicleefficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towardsmeeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 AcknowledgmentThe authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. ChristopherYang for their suggestions over the course of this project. This work was funded by the Laboratory DirectedResearch and Development program at Sandia National Laboratories.4

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46 p.

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  • Report No.: SAND2012-7863
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 1117264
  • Archival Resource Key: ark:/67531/metadc869311

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  • September 1, 2012

Added to The UNT Digital Library

  • Sept. 16, 2016, 12:32 a.m.

Description Last Updated

  • Feb. 17, 2017, 4:25 p.m.

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Barter, Garrett; Reichmuth, David; Westbrook, Jessica; Malczynski, Leonard A.; Yoshimura, Ann S.; Peterson, Meghan et al. Transportation Energy Pathways LDRD., report, September 1, 2012; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc869311/: accessed August 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.