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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