Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data

Wu, Xing; Aviquzzaman, Md.; Lin, Zhenhong

doi:10.1016/j.trc.2015.05.008

Title: Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data

Journal Article · Fri May 29 00:00:00 EDT 2015 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2015.05.008· OSTI ID:1286863

Wu, Xing ^[1]; Aviquzzaman, Md. ^[1]; Lin, Zhenhong ^[2]

Lamar University, Beaumont, TX (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)

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.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1286863

Journal Information:: Transportation Research Part C: Emerging Technologies, Vol. 57; ISSN 0968-090X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 30 works

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The impact of reliable range estimation on battery electric vehicle feasibility Dong, Jing; Wu, Xing; Liu, Changzheng International Journal of Sustainable Transportation, Vol. 14, Issue 11 https://doi.org/10.1080/15568318.2019.1639085	journal	November 2019
Distributed Charging Prioritization Methodology Based on Evolutionary Computation and Virtual Power Plants to Integrate Electric Vehicle Fleets on Smart Grids Guerrero, J. I.; Personal, Enrique; García, Antonio Energies, Vol. 12, Issue 12 https://doi.org/10.3390/en12122402	journal	June 2019
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Title: Analysis of plug-in hybrid electric vehicles' utility factors using GPS-based longitudinal travel data

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