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Title: Transportation Energy Pathways LDRD.

Abstract

This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuelmore » 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 technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended 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 aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency 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 towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1117264
Report Number(s):
SAND2012-7863
498454
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Barter, Garrett., Reichmuth, David., Westbrook, Jessica, Malczynski, Leonard A., Yoshimura, Ann S., Peterson, Meghan B., West, Todd H., Manley, Dawn Kataoka, Guzman, Katherine Dunphy, Edwards, Donna M., and Hines, Valerie Ann-Peters. Transportation Energy Pathways LDRD.. United States: N. p., 2012. Web. doi:10.2172/1117264.
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Barter, Garrett., Reichmuth, David., Westbrook, Jessica, Malczynski, Leonard A., Yoshimura, Ann S., Peterson, Meghan B., West, Todd H., Manley, Dawn Kataoka, Guzman, Katherine Dunphy, Edwards, Donna M., & Hines, Valerie Ann-Peters. Transportation Energy Pathways LDRD.. United States. doi:10.2172/1117264.
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Barter, Garrett., Reichmuth, David., Westbrook, Jessica, Malczynski, Leonard A., Yoshimura, Ann S., Peterson, Meghan B., West, Todd H., Manley, Dawn Kataoka, Guzman, Katherine Dunphy, Edwards, Donna M., and Hines, Valerie Ann-Peters. Sat . "Transportation Energy Pathways LDRD.". United States. doi:10.2172/1117264. https://www.osti.gov/servlets/purl/1117264.
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@article{osti_1117264,
title = {Transportation Energy Pathways LDRD.},
author = {Barter, Garrett. and Reichmuth, David. and Westbrook, Jessica and Malczynski, Leonard A. and Yoshimura, Ann S. and Peterson, Meghan B. and West, Todd H. and Manley, Dawn Kataoka and Guzman, Katherine Dunphy and Edwards, Donna M. and Hines, Valerie Ann-Peters},
abstractNote = {This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 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 technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended 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 aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency 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 towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.},
doi = {10.2172/1117264},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Sep 01 00:00:00 EDT 2012},
month = {Sat Sep 01 00:00:00 EDT 2012}
}
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DOI:  10.2172/1117264
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