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Cost analysis of plug-in hybrid electric vehicles using GPS-based longitudinal travel data

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  • Wu, Xing
  • Dong, Jing
  • Lin, Zhenhong

Abstract

Using spatial, longitudinal travel data of 415 vehicles over 3–18 months in the Seattle metropolitan area, this paper estimates the operating costs of plug-in hybrid electric vehicles (PHEVs) of various electric ranges (10, 20, 30, and 40 miles) for 3, 5, and 10 years of payback period, considering different charging infrastructure deployment levels and gasoline prices. Some key findings were made. (1) PHEVs could help save around 60% or 40% in energy costs, compared with conventional gasoline vehicles (CGVs) or hybrid electric vehicles (HEVs), respectively. However, for motorists whose daily vehicle miles traveled (DVMT) is significant, HEVs may be even a better choice than PHEV40s, particularly in areas that lack a public charging infrastructure. (2) The incremental battery cost of large-battery PHEVs is difficult to justify based on the incremental savings of PHEVs' operating costs unless a subsidy is offered for large-battery PHEVs. (3) When the price of gasoline increases from $4/gallon to $5/gallon, the number of drivers who benefit from a larger battery increases significantly. (4) Although quick chargers can reduce charging time, they contribute little to energy cost savings for PHEVs, as opposed to Level-II chargers.

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  • Wu, Xing & Dong, Jing & Lin, Zhenhong, 2014. "Cost analysis of plug-in hybrid electric vehicles using GPS-based longitudinal travel data," Energy Policy, Elsevier, vol. 68(C), pages 206-217.
    • Handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:206-217
    DOI: 10.1016/j.enpol.2013.12.054
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    References listed on IDEAS

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    1. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
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    1. Wu, Xing, 2018. "Role of workplace charging opportunities on adoption of plug-in electric vehicles – Analysis based on GPS-based longitudinal travel data," Energy Policy, Elsevier, vol. 114(C), pages 367-379.
    2. Mayyas, Abdel Ra'ouf & Kumar, Sushil & Pisu, Pierluigi & Rios, Jacqueline & Jethani, Puneet, 2017. "Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach," Applied Energy, Elsevier, vol. 204(C), pages 287-302.
    3. Makena Coffman & Paul Bernstein & Sherilyn Wee, 2017. "Electric vehicles revisited: a review of factors that affect adoption," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 79-93, January.
    4. Dimitrova, Zlatina & Maréchal, François, 2015. "Energy integration on multi-periods and multi-usages for hybrid electric and thermal powertrains," Energy, Elsevier, vol. 83(C), pages 539-550.
    5. Wee, Sherilyn & Coffman, Makena & Allen, Scott, 2020. "EV driver characteristics: Evidence from Hawaii," Transport Policy, Elsevier, vol. 87(C), pages 33-40.
    6. Dimitrova, Zlatina & Maréchal, François, 2015. "Techno-economic design of hybrid electric vehicles using multi objective optimization techniques," Energy, Elsevier, vol. 91(C), pages 630-644.
    7. Wu, Geng & Inderbitzin, Alessandro & Bening, Catharina, 2015. "Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis and projection across market segments," Energy Policy, Elsevier, vol. 80(C), pages 196-214.
    8. Yijiao Wang & Guoguang Zhou & Ting Li & Xiao Wei, 2019. "Comprehensive Evaluation of the Sustainable Development of Battery Electric Vehicles in China," Sustainability, MDPI, vol. 11(20), pages 1-27, October.
    9. Makena Coffman & Scott Allen & Sherilyn Wee, 2018. "Who are Driving Electric Vehicles? An analysis of factors that affect EV adoption in Hawaii," Working Papers 2018-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    10. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    11. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2016. "Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios," Applied Energy, Elsevier, vol. 177(C), pages 366-383.
    12. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    13. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    14. Zhiwei Chen & Yucong Hu & Jutint Li & Xing Wu, 2020. "Optimal Deployment of Electric Bicycle Sharing Stations: Model Formulation and Solution Technique," Networks and Spatial Economics, Springer, vol. 20(1), pages 99-136, March.
    15. Roberto Finesso & Daniela Misul & Ezio Spessa & Mattia Venditti, 2018. "Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO 2 Emission Minimization," Energies, MDPI, vol. 11(7), pages 1-28, July.
    16. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2014. "Layout design and energetic analysis of a complex diesel parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 134(C), pages 573-588.

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