The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.
Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/42882 |
Date | 27 November 2013 |
Creators | Raykin, Leonid |
Contributors | MacLean, Heather L., Roorda, Matthew J. |
Source Sets | University of Toronto |
Language | en_US |
Detected Language | English |
Type | Thesis |
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