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Role of workplace charging opportunities on adoption of plug-in electric vehicles – Analysis based on GPS-based longitudinal travel data

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  • Wu, Xing

Abstract

The benefit of using a plug-in electric vehicle (PEV) comes from its ability to substitute gasoline with electricity in operation. This paper studies the impact of workplace charges on the adoption of two types of PEVs: battery electric vehicle (BEV) and plug-in electric vehicle (PHEV). BEV users care more about the range anxiety issue. PHEV users, on the other hand, focus on the cost savings. The GPS-based longitude travel data of 143 vehicles covering 20 days to 18 months were employed in this paper. The dataset provides the detailed spatial and temporal travel information of a vehicle. With the known household locations, home-to-home tours were built for each vehicle, because home charges is usually regarded as a basic charging pattern. The detailed travel information of each vehicle was further used to help determine the workplace locations by analyzing its travel behavior over a period. Then, some home-to-home tours were broken into a series of home-to-work, work-to-home and work-to-work (if applicable) tours. These tours were used for analyzing the impact of workplace charges on the adoption of BEVs and PHEVs in population, respectively, considering three different levels of charger powers, as well as different price of gasoline.

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  • 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.
    • Handle: RePEc:eee:enepol:v:114:y:2018:i:c:p:367-379
    DOI: 10.1016/j.enpol.2017.12.015
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    References listed on IDEAS

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    1. Egbue, Ona & Long, Suzanna, 2012. "Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions," Energy Policy, Elsevier, vol. 48(C), pages 717-729.
    2. 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.
    3. 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.
    4. Tulpule, Pinak J. & Marano, Vincenzo & Yurkovich, Stephen & Rizzoni, Giorgio, 2013. "Economic and environmental impacts of a PV powered workplace parking garage charging station," Applied Energy, Elsevier, vol. 108(C), pages 323-332.
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    Cited by:

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    2. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    3. Zhang, Ziqi & Chen, Zhong & Xing, Qiang & Ji, Zhenya & Zhang, Tian, 2022. "Evaluation of the multi-dimensional growth potential of China's public charging facilities for electric vehicles through 2030," Utilities Policy, Elsevier, vol. 75(C).
    4. Miguel-Angel Perea-Moreno & Quetzalcoatl Hernandez-Escobedo & Alberto-Jesus Perea-Moreno, 2018. "Renewable Energy in Urban Areas: Worldwide Research Trends," Energies, MDPI, vol. 11(3), pages 1-19, March.
    5. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    6. Gudmunds, D. & Nyholm, E. & Taljegard, M. & Odenberger, M., 2020. "Self-consumption and self-sufficiency for household solar producers when introducing an electric vehicle," Renewable Energy, Elsevier, vol. 148(C), pages 1200-1215.

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