A Plug-in Hybrid Consumer Choice Model with Detailed Market Segmentation

This paper develops a consumer choice model to project U.S. demand for plug-in hybrid vehicles (PHEV) in competition among 26 light-duty vehicle technologies over the period 2005-2050. New car buyers are disaggregated by region, residential area, attitude toward technology risk, vehicle usage intensity, home parking and work recharging. The nested multinomial logit (NMNL) model of vehicle choice considers daily vehicle usage distribution, refueling and recharging availability, technology learning by doing, and vehicle model and make availability. Illustrative results are presented for a Base Case, calibrated to the Annual Energy Outlook (AEO) 2009 Reference Updated Case, and an optimistic technology scenario reflecting achievement of U.S. Department of Energy’s (DOE’s) FreedomCAR goals. PHEV market success is highly dependent on the degree of technological progress assumed. PHEV sales will reach one million by 2037 in the Base Case and by 2020 in the FreedomCARGoals Case. In the FreedomCARGoals Case, PHEV cumulative sales will reach 1.5 million by 2015 and together with efficiency improvement of other technologies, reduce petroleum use in 2050 by about 45% from the 2005 level. The PHEV share appears to be most sensitive to recharging availability, technology attitude and vehicle usage intensity. The fast growth of PHEV sales also helps bring down the battery cost for electric vehicles (EVs), resulting in a significant EV market share after 2040.

[1]  M. Delucchi,et al.  Fuel Economy: The Case for Market Failure , 2008 .

[2]  Kara M. Kockelman,et al.  Greenhouse gas emission control options: Assessing transportation and electricity generation technologies and policies to stabilize climate change , 2009 .

[3]  David L. Greene,et al.  TAFV Alternative Fuels and Vehicles Choice Model Documentation , 2001 .

[4]  Jonn Axsen,et al.  Early U.S. Market for Plug-In Hybrid Electric Vehicles , 2009 .

[5]  J. Ogden,et al.  The least-cost hydrogen for Southern California , 2008 .

[6]  E. Rogers.  Diffusion of Innovations , 1962 .

[7]  Tony Markel,et al.  Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology , 2007 .

[8]  N. Stern.  The Economics of Climate Change: Implications of Climate Change for Development , 2007 .

[9]  Tina N. Barrett,et al.  The American Recovery and Reinvestment Act of 2009 , 2009 .

[10]  David L. Greene,et al.  Integrated Analysis of Market Transformation Scenarios with HyTrans , 2007 .

[11]  David L. Greene,et al.  Estimating daily vehicle usage distributions and the implications for limited-range vehicles , 1985 .

[12]  Constantine Samaras,et al.  Policies to Promote Plug-in Hybrid Electric Vehicles for Greenhouse Gas Emissions Reductions and Oil Displacement , 2009 .

[13]  Edward S. Rubin,et al.  Uncertainties in Technology Experience Curves for Integrated Assessment Models , 2007 .

[14]  Sujit Das,et al.  Multi-Path Transportation Futures Study: Vehicle Characterization and Scenario Analyses. Appendix E. Other NEMS-MP Results or the Base Case and Scenarios , 2009 .

[15]  Daniel Sperling,et al.  Using Geographic Information Systems to Evaluate Siting and Networks of Hydrogen Stations , 2004 .

[16]  John B. Heywood,et al.  A Comparative Assessment of Electric Propulsion Systems in the 2030 US Light-Duty Vehicle Fleet , 2008 .

[17]  C. Wene.  Experience Curves for Energy Technology Policy , 2000 .

[18]  David L. Greene,et al.  Systems Analysis of Hydrogen Transition with HyTrans , 2006 .

[19]  Anant D Vyas,et al.  Plug-In Hybrid Electric Vehicles' Potential for Petroleum Use Reduction: Issues Involved in Developing Reliable Estimates , 2009 .

[20]  Zhenhong Lin,et al.  The fuel-travel-back approach to hydrogen station siting , 2008 .

[21]  Vincent Mahieu,et al.  Well-to-wheels analysis of future automotive fuels and powertrains in the european context , 2004 .