Location optimisation method for fast-charging stations along national roads
Csaba Csiszár,
Bálint Csonka,
Dávid Földes,
Ervin Wirth and
Tamás Lovas
Journal of Transport Geography, 2020, vol. 88, issue C
Abstract:
Limited range of electric vehicles is still a huge barrier compared to conventional vehicles. A well-established charging station network, which is derived from users' charging demand, facilitates the spread of electric vehicles and lessens the range anxiety. Several methods have been developed for locating fast-charging stations along national roads in Europe according to the given objective function. In this paper, an arc-based location optimisation method realized by using a geographic information system and greedy algorithm is presented. An ‘oil stain’ deployment strategy is used to achieve even coverage with the minimum number of fast-charging stations along the roads. Several demographic, neighbourhood, and transport-related attributes, as well as the available services that influence the utilization of a fast-charging station, have been identified and their effects have been revealed in a systematic approach. The developed multi-criteria decision-making method has been applied to evaluate the rest areas along motorways and main roads and to propose deployment locations for fast-charging stations. The method was applied for Hungary as a case study and validated using real origin-destination (O-D) data. By the application of the locating method, the user can specify a network character by geographic parameters. The method can be especially beneficial if the O-D flows are unknown. Furthermore, the even distribution of the stations contributes to the high utilization of the fast-charging stations.
Keywords: Electric vehicle; Fast-charging infrastructure; Arc-based; Oil stain strategy (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:88:y:2020:i:c:s0966692319303801
DOI: 10.1016/j.jtrangeo.2020.102833
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