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Impact of Powertrain Type on Potential Life Cycle Greenhouse Gas Emission Reductions from a Real World Lightweight Glider
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This study investigates the life cycle greenhouse gas (GHG) emissions of a set of vehicles using two real-world gliders (vehicles without powertrains or batteries); a steel-intensive 2013 Ford Fusion glider and a multi material lightweight vehicle (MMLV) glider that utilizes significantly more aluminum and carbon fiber. These gliders are used to develop lightweight and conventional models of internal combustion engine vehicles (ICV), hybrid electric vehicles (HEV), and battery electric vehicles (BEV). Our results show that the MMLV glider can reduce life cycle GHG emissions despite its use of lightweight materials, which can be carbon intensive to produce, because the glider enables a decrease in fuel (production and use) cycle emissions. However, the fuel savings, and thus life cycle GHG emission reductions, differ substantially depending on powertrain type. Compared to ICVs, the high efficiency of HEVs decreases the potential fuel savings. BEVs are more efficient than HEVs but require heavy batteries to provide an acceptable driving range. A lightweight glider can allow a smaller battery to be used without sacrificing driving range. Battery downsizing is a secondary source of mass reduction that further decreases fuel use. A comparison of our results with those of other studies reveals inconsistencies and lack of powertrain-specific assumptions in the literature, which can mischaracterize the GHG emissions associated with producing lightweight vehicles, and those from potential fuel savings.
CitationLuk, J., Kim, H., De Kleine, R., Wallington, T. et al., "Impact of Powertrain Type on Potential Life Cycle Greenhouse Gas Emission Reductions from a Real World Lightweight Glider," SAE Technical Paper 2017-01-1274, 2017, https://doi.org/10.4271/2017-01-1274.
Data Sets - Support Documents
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