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Latest revision as of 18:04, 28 January 2021

Abstract

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V.

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Original document

The different versions of the original document can be found in:

• http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19238

• https://doi.org/10.1016/j.procs.2019.09.446 under the license cc-by-nc-nd

• https://api.elsevier.com/content/article/PII:S1877050919316618?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S1877050919316618?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.procs.2019.09.446 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S1877050919316618,

https://dblp.uni-trier.de/db/conf/euspn/euspn2019.html#FredrikssonDH19,

http://www.diva-portal.org/smash/record.jsf?pid=diva2:1394942,

https://muep.mau.se/handle/2043/30487,

https://academic.microsoft.com/#/detail/2990366762