Evaluating the likely temporal variation in electric vehicle charging demand at popular amenities using smartphone locational data

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Dixon, James and Elders, Ian and Bell, Keith (2020) Evaluating the likely temporal variation in electric vehicle charging demand at popular amenities using smartphone locational data. IET Intelligent Transport Systems, 14 (6). pp. 504-510. ISSN 1751-9578 (https://doi.org/10.1049/iet-its.2019.0351)

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Abstract

'Destination charging' - in which drivers charge their battery electric vehicles (EVs) while parked at amenities such as supermarkets, shopping centres, gyms and cinemas - has the potential to accelerate EV uptake. This study presents a Monte Carlo-based method for the characterisation of EV destination charging at these locations based on smartphone users' anonymised positional data captured in the Google Maps Popular Times feature. Unlike the use of household and travel surveys, from which most academic works on the subject are based, these data represent individuals' actual movements rather than how they might recall or divulge them. Through a fleet EV charging approach proposed in this study, likely electrical demand profiles for EV destination charging at different amenities are presented. Use of the method is presented first for a generic characterisation of EV charging in the car parks of gyms, based on a sample of over 2000 gyms in around major UK cities, and second for a specific characterisation of hypothetical EV charging infrastructure installed at a large UK shopping centre to investigate the impact of varying the grid and converter capacity on the expected charging demand and level of service provision to the vehicles charging there.

ORCID iDs

Dixon, James ORCID logoORCID: https://orcid.org/0000-0001-8930-805X, Elders, Ian ORCID logoORCID: https://orcid.org/0000-0002-6060-9235 and Bell, Keith ORCID logoORCID: https://orcid.org/0000-0001-9612-7345;
  • Item type:Article
    ID code:71417
    Dates:
    Date
    Event
    1 June 2020
    Published
    6 February 2020
    Published Online
    5 February 2020
    Accepted
    Notes:This paper is a postprint of a paper submitted to and accepted for publication in IET Intelligent Transport Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:11 Feb 2020 09:58
    Last modified:16 Dec 2024 02:12
    URI:https://strathprints.strath.ac.uk/id/eprint/71417
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