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A review of spatial localization methodologies for the electric vehicle charging infrastructure
AuthorPagany, Raphaela ; Camargo, Luis Ramirez ; Dorner, Wolfgang
Published in
International Journal of Sustainable Transportation, London, 2018, Vol. 2018, page 1-17
PublishedLondon : Taylor & Francis, 2018
Document typeJournal Article
Keywords (EN)Charging station / electric vehicle / GIS / localization / optimization / spatial modeling
URNurn:nbn:at:at-ubs:3-10283 Persistent Identifier (URN)
 The work is publicly available
A review of spatial localization methodologies for the electric vehicle charging infrastructure [2.07 mb]
Abstract (English)

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problems spatial dimension, and derives perspectives for further research.

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