skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Quantifying the impacts of micro- and mild- hybrid vehicle technologies on fleetwide fuel economy and electrification

Journal Article · · eTransportation
ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
+ Show Author Affiliations

Micro- and mild-hybridization (jointly labeled as M-HEV) is gaining popularity as a cost-effective technology for fuel economy improvement, but whether and how M-HEV may compete against less efficient conventional internal combustion engine vehicles (ICEV), more efficient full hybrid electric vehicles (HEV), and plug-in electric vehicles (PEV) is not well understood. As such, this study aims at evaluating the impact of the market adoption of M-HEV on the average fuel economy of the new vehicle fleet and on the sales share of PEVs. The study reviews recent sales trends and market forecasts, and uses published estimates of manufacturing cost and fuel economy of M-HEV with an existing discrete choice model (Market Acceptance of Advanced Automotive Technologies or MA3T) to project the market penetration and impacts of M-HEV under different scenarios of M-HEV choice positions, designed to enhance conclusion robustness. It is found that among engine-based powertrain choices, micro-HEV appears to be the most cost-effective, followed by ICEVs, mild-HEV and finally full HEVs. M-HEV technologies are likely to improve fleetwide average fuel economy without significant adverse effects on sales of plug-in electric vehicles, and are likely to remain highly competitive outside PEVs through 2050.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office
Grant/Contract Number:
AC05-00OR22725; AC02-06CH11357
OSTI ID:
1619014
Alternate ID(s):
OSTI ID: 1618014; OSTI ID: 1774529
Journal Information:
eTransportation, Vol. 4, Issue C; ISSN 2590-1168
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (10)

The impact of government incentives for hybrid-electric vehicles: Evidence from US states journal March 2009
GASOLINE PRICES, GOVERNMENT SUPPORT, AND THE DEMAND FOR HYBRID VEHICLES IN THE UNITED STATES*: hybrid vehicle demand journal February 2011
Performance, Cost, and Market Share of Conventional Vehicle Efficiency Technologies?: Retrospective Comparison of Regulatory Document Projections for Corporate Average Fuel Economy and Greenhouse Gas Standards
  • Xie, Fei; Lin, Zhenhong; Nealer, Rachael
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 2628, Issue 1 https://doi.org/10.3141/2628-08
journal January 2017
Light-duty plug-in electric vehicles in China: An overview on the market and its comparisons to the United States journal September 2019
Influence of the stop/start system on CO2 emissions of a diesel vehicle in urban traffic journal March 2011
A comprehensive review on choice of hybrid vehicles and power converters, control strategies for hybrid electric vehicles journal December 2017
Friction Reduction and Reliability for Engines Bearings journal July 2015
Market-driven automotive industry compliance with fuel economy and greenhouse gas standards: Analysis based on consumer choice journal September 2017
Analyzing the sensitivity of hydrogen vehicle sales to consumers' preferences journal December 2013
Hydrogen vehicles: Impacts of DOE technical targets on market acceptance and societal benefits journal June 2013

Similar Records

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
Hybrid electric vehicle
Micro hybridization
Fuel economy
Vehicle market penetration
Discrete choice model