Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data |
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| Institution: | 1. Lamar University, Department of Civil and Environmental Engineering, PO BOX 10024, Beaumont, TX 77710, United States;2. Oak Ridge National Laboratory, National Transportation Research Center, 2360 Cherahala Boulevard, Knoxville, TN 37932, United States;1. Department of Urban Planning and Environment, KTH Royal Institute of Technology, Teknikringen 10, 100 44 Stockholm, Sweden;2. Transportation Research Institute, Hasselt University, Wetenschapspark 5, bus 6, 3590 Diepenbeek, Belgium;1. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., MS90R1121B, Berkeley, CA 94720, USA;2. Institute of Automation, Chinese Academy of Sciences, Beijing, China;3. Department of Electrical Engineering, University of Washington, Seattle, WA, USA;1. School of Data Science, City University of Hong Kong, Hong Kong;2. School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China;3. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China |
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| 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 and electricity. Moreover, 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 travel 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. On the other hand, 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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| Keywords: | Plug-in hybrid electric vehicle Utility factor GPS-based travel data Home-to-home tour |
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