To cluster the E-mobility recharging facilities (RFs)

Elbanhawy, Eiman (2015). To cluster the E-mobility recharging facilities (RFs). In: Leal Filho, Walter and Kotter, Richard eds. E-Mobility in Europe: Trends and Good Practice. Green Energy and Technology. Cham: Springer, pp. 255–279.




The world is witnessing an accelerating expansion of urban areas and intensive urbanisation. The robust relation between transport infrastructure and urban planning is reflected in how integrated and reliable a system is within the urban fabric. Designing an integrated infrastructure to support full electric vehicle (EV) use is a crucial matter, which worries planning authorities, policy makers, as well as current and potential users. Reducing range anxiety by facilitating access to public recharging facilities is designed to overcome the main barrier that stops potential users to utilise EVs. The unvertainty of having a reliable and integrated charging infrastructure also presents hurdles, and slows down the growing trend of smart ecosystems and sustainable urban communities as a whole. Automotive, battery and utility technologies have formed the cornerstone of the EV industry to compete with currently mainstream means of transport, and to gain more prominence within many regions. Strategically locating public EV charging points will help to pave the way for better market penetration of EVs. This paper analyses real information about EV users in one of these metropolitan areas. A case study of 13 charging points with 48 EV users located in the inner urban core (NE1 postcode district) of a metropolitan area in North East England, the city of Newcastle upon Tyne, incorporating space-time analysis of the EV population, is presented here. Information about usage and charging patterns is collected from the main local service provider in North East England, Charge Your Car (CYC) Ltd. The methodology employed is a clustering analysis. It is conducted as a dimensional analysis technique for data mining and for significant analysis of quantitative data sets. A spatial and temporal analysis of charging patterns is conducted using SPSS and predictive analytics software. The study outcomes provide recommendations, exploring design theory and the implementation of public EV recharging infrastructure. The chapter presents a methodological approach useful for planning authorities, policy makers and commercial agents in evaluating and measuring the degree of usability fo the public electric mobility system.

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