Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data
Abstract
The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline. In addition, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist's daily distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3-18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home andmore »
- Authors:
-
- Lamar University, Beaumont, TX (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1286863
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Transportation Research Part C: Emerging Technologies
- Additional Journal Information:
- Journal Volume: 57; Journal ID: ISSN 0968-090X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS; 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Plug-in hybrid electric vehicle; Utility factor; GPS-based travel data; Home-to-home tour
Citation Formats
Wu, Xing, Aviquzzaman, Md., and Lin, Zhenhong. Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data. United States: N. p., 2015.
Web. doi:10.1016/j.trc.2015.05.008.
Wu, Xing, Aviquzzaman, Md., & Lin, Zhenhong. Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data. United States. https://doi.org/10.1016/j.trc.2015.05.008
Wu, Xing, Aviquzzaman, Md., and Lin, Zhenhong. Fri .
"Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data". United States. https://doi.org/10.1016/j.trc.2015.05.008. https://www.osti.gov/servlets/purl/1286863.
@article{osti_1286863,
title = {Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data},
author = {Wu, Xing and Aviquzzaman, Md. and Lin, Zhenhong},
abstractNote = {The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline. In addition, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist's daily distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3-18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. However, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.},
doi = {10.1016/j.trc.2015.05.008},
journal = {Transportation Research Part C: Emerging Technologies},
number = ,
volume = 57,
place = {United States},
year = {Fri May 29 00:00:00 EDT 2015},
month = {Fri May 29 00:00:00 EDT 2015}
}
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