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Title: Comparison of Vehicle Choice Models

Abstract

Five consumer vehicle choice models that give projections of future sales shares of light-duty vehicles were compared by running each model using the same inputs, where possible, for two scenarios. The five models compared — LVCFlex, MA3T, LAVE-Trans, ParaChoice, and ADOPT — have been used in support of the Energy Efficiency and Renewable Energy (EERE) Vehicle Technologies Office in analyses of future light-duty vehicle markets under different assumptions about future vehicle technologies and market conditions. The models give projections of sales shares by powertrain technology. Projections made using common, but not identical, inputs showed qualitative agreement, with the exception of ADOPT. ADOPT estimated somewhat lower advanced vehicle shares, mostly composed of hybrid electric vehicles. Other models projected large shares of multiple advanced vehicle powertrains. Projections of models differed in significant ways, including how different technologies penetrated cars and light trucks. Since the models are constructed differently and take different inputs, not all inputs were identical, but were the same or very similar where possible. Projections by all models were in close agreement only in the first few years. Although the projections from LVCFlex, MA3T, LAVE-Trans, and ParaChoice were in qualitative agreement, there were significant differences in sales shares given bymore » the different models for individual powertrain types, particularly in later years (2030 and later). For example, projected sales shares of conventional spark-ignition vehicles in 2030 for a given scenario ranged from 35% to 74%. Reasons for such differences are discussed, recognizing that these models were not developed to give quantitatively accurate predictions of future sales shares, but to represent vehicles markets realistically and capture the connections between sales and important influences. Model features were also compared at a high level, and suggestions for further comparison of models are given to enable better understanding of how different features and algorithms used in these models may give different projections.« less

Authors:
 [1];  [2];  [3];  [4];  [4];  [5];  [3]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  5. Energetics Incorporated, Columbia, MD (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1411851
Report Number(s):
SAND2017-13044R; ANL/ESD-17/19
659226
DOE Contract Number:  
NA0003525; AC02-06CH11357; AC36-08GO28308; AC05-00OR22725
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 33 ADVANCED PROPULSION SYSTEMS

Citation Formats

Stephens, Thomas S., Levinson, Rebecca S., Brooker, Aaron, Liu, Changzheng, Lin, Zhenhong, Birky, Alicia, and Kontou, Eleftheria. Comparison of Vehicle Choice Models. United States: N. p., 2017. Web. doi:10.2172/1411851.
Stephens, Thomas S., Levinson, Rebecca S., Brooker, Aaron, Liu, Changzheng, Lin, Zhenhong, Birky, Alicia, & Kontou, Eleftheria. Comparison of Vehicle Choice Models. United States. doi:10.2172/1411851.
Stephens, Thomas S., Levinson, Rebecca S., Brooker, Aaron, Liu, Changzheng, Lin, Zhenhong, Birky, Alicia, and Kontou, Eleftheria. Tue . "Comparison of Vehicle Choice Models". United States. doi:10.2172/1411851. https://www.osti.gov/servlets/purl/1411851.
@article{osti_1411851,
title = {Comparison of Vehicle Choice Models},
author = {Stephens, Thomas S. and Levinson, Rebecca S. and Brooker, Aaron and Liu, Changzheng and Lin, Zhenhong and Birky, Alicia and Kontou, Eleftheria},
abstractNote = {Five consumer vehicle choice models that give projections of future sales shares of light-duty vehicles were compared by running each model using the same inputs, where possible, for two scenarios. The five models compared — LVCFlex, MA3T, LAVE-Trans, ParaChoice, and ADOPT — have been used in support of the Energy Efficiency and Renewable Energy (EERE) Vehicle Technologies Office in analyses of future light-duty vehicle markets under different assumptions about future vehicle technologies and market conditions. The models give projections of sales shares by powertrain technology. Projections made using common, but not identical, inputs showed qualitative agreement, with the exception of ADOPT. ADOPT estimated somewhat lower advanced vehicle shares, mostly composed of hybrid electric vehicles. Other models projected large shares of multiple advanced vehicle powertrains. Projections of models differed in significant ways, including how different technologies penetrated cars and light trucks. Since the models are constructed differently and take different inputs, not all inputs were identical, but were the same or very similar where possible. Projections by all models were in close agreement only in the first few years. Although the projections from LVCFlex, MA3T, LAVE-Trans, and ParaChoice were in qualitative agreement, there were significant differences in sales shares given by the different models for individual powertrain types, particularly in later years (2030 and later). For example, projected sales shares of conventional spark-ignition vehicles in 2030 for a given scenario ranged from 35% to 74%. Reasons for such differences are discussed, recognizing that these models were not developed to give quantitatively accurate predictions of future sales shares, but to represent vehicles markets realistically and capture the connections between sales and important influences. Model features were also compared at a high level, and suggestions for further comparison of models are given to enable better understanding of how different features and algorithms used in these models may give different projections.},
doi = {10.2172/1411851},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 31 00:00:00 EDT 2017},
month = {Tue Oct 31 00:00:00 EDT 2017}
}

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