Abstract
On-street Vehicle-to-Grid (V2G) allows battery electric vehicles (BEVs) to park on the street, charge from the electricity grid, and give electricity back to the grid. It could encourage people without off-street parking at home to adopt electric vehicles. On-street V2G is technically feasible, but there is a lack of understanding of consumer perceptions of On-street V2G features and services. This study aims to quantitatively and empirically explore people’s attitudes towards On-street V2G with a focus on preference and willingness-to-pay. An online survey was carried out and 495 successful responses were collected. In the survey, a video clip that explained On-street V2G was included to better sensitise participants to the futuristic scenario of On-street V2G. This study found that ‘required plug-in hours per month’ was viewed as more important regarding preference for On-street V2G, while ‘minimum level of battery guaranteed’ was more important regarding willingness-to-pay. People hold opposite views towards these two features concerning preference and willingness-to-pay. The theoretical contribution, practical implications, and future research directions are discussed.
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Acknowledgements
This work was supported by the Innovate UK funded project V2Street [grant number 104224] and represents research carried out at Loughborough University. We thank the project partners Durham County Council, E-Car Club, EDF Energy, Imperial College London, Southend on Sea Borough Council, Ubitricity, UKPN, and Upside Energy for their contributions to the initial workshop and the development of the study reported in this paper. The authors are also appreciative to Nanjing University of Aeronautics and Astronautics for the Grant YAH20099, which enabled the continuing involvement of the primary author in the research beyond the initial project completion date.
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Zhang, L., Ross, T., Cain, R. (2021). Designing a New Electric Vehicle Charging System: People’s Preference and Willingness-To-Pay. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2021. Lecture Notes in Computer Science(), vol 12791. Springer, Cham. https://doi.org/10.1007/978-3-030-78358-7_12
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