Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey
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Pollution, climate change, fast natural resource depletion, deforestation and global warming have become major worldwide problems relevant with the petroleum-based powered vehicles and alternatives for this conventional transportation type have been started to change in the last decade. In this modification process, electric vehicles (EVs) have a leading position due to their low damage effect to the environment. Selecting the most sustainable location for charging station for EVs plays an important role in the life cycle of them. This process needs to consider some conflicting criteria and has a complex decision problem that can be modeled as a multi-criteria decision-making problem. The inclusion of such criteria in a location selection requires the fuzzy sets to be used in the decision-making methodology. For this aim, intuitionistic fuzzy sets have been used in this paper. By the way, a decision-making procedure based on intuitionistic fuzzy sets and consists of the decision-making trial and evaluation laboratory, analytic hierarchy process and technique for order preference by similarity to ideal solution has been suggested for the location selection of charge stations. The proposed fuzzy-based model is applied to a case study for Istanbul in Turkey.
KeywordsElectric vehicles charging stations Location selection Intuitionistic fuzzy sets Decision making DEMATEL AHP TOPSIS
Compliance with ethical standards
Conflict of interest
All the authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 1.Eisel M, Schmidt J, Kolbe LM (2014) Finding suitable locations for charging stations. In: 2014 IEEE international electric vehicle conference (IEVC). IEEE, pp 1–8Google Scholar
- 5.Yi Z, Bauer PH (2014) Energy consumption model and charging station placement for electric vehicles. In: 3rd International conference on smart grids and green IT systems, pp 150–156Google Scholar
- 8.Xu Q, Cai T, Liu Y (2016) Location planning of charging stations for electric vehicles based on drivers behaviors’ and travel chain. Autom Electric Power Syst 4:59–65Google Scholar
- 9.Jia L, Hu Z, Song Y, Luo Z (2012) Optimal siting and sizing of electric vehicle charging stations. In: 2012 IEEE international on electric vehicle conference (IEVC). IEEE, pp 1–6Google Scholar
- 12.Erdoǧan M, Bilisik ON, Kaya I (2018) A new fuzzy decision-making procedure to prioritization of the brand city candidates for Turkey. J Mult Valued Logic Soft Comput 30(1):1–28Google Scholar
- 18.Hui W (2013) Some operations on interval-valued intuitionistic fuzzy sets. In: 2013 Fifth international conference on computational and information sciences (ICCIS). IEEE, pp 832–834Google Scholar
- 20.Zhao T, Xiao J (2012) Type-2 intuitionistic fuzzy sets. Control Theory Appl 29(9):1215–1222Google Scholar
- 21.Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, pp 1–8Google Scholar
- 22.Si SL, You XY, Liu HC, Zhang P (2018) DEMATEL technique: a systematic review of the state-of-the-art literature on methodologies and applications. Math Probl EngGoogle Scholar
- 25.Pan F, Bent R, Berscheid A, Izraelevitz D (2010) Locating PHEV exchange stations in V2G. In: 2010 First IEEE international conference on smart grid communications (SmartGridComm). IEEE, pp 173–178Google Scholar
- 28.Wirges J, Linder S, Kessler A (2012) Modelling the development of a regional charging infrastructure for electric vehicles in time and space. Eur J Transp Infrastruct Res 12:391–416Google Scholar
- 29.Andrews M, Dogru MK, Hobby JD, Jin Y, Tucci H (2013) Modeling and optimization for electric vehicle charging infrastructure. In: IEEE innovative smart grid technologies conferenceGoogle Scholar
- 30.Pazouki S, Mohsenzadeh A, Haghifam MR (2013) Optimal planning of PEVs charging stations and demand response programs considering distribution and traffic networks. In: 2013 Smart grid conference (SGC). IEEE, pp 90–95Google Scholar
- 32.Lam A, Leung YW, Chu X (2013) Electric vehicle charging station placement. In: 2013 IEEE international conference on smart grid communications (SmartGridComm). IEEE, pp 510–515Google Scholar
- 33.Wagner S, Götzinger M, Neumann D (2013) Optimal location of charging stations in smart cities: a point of interest based approach. In: 34th International conference on information systems (ICIS), MilanGoogle Scholar
- 34.Micari S, Napoli G, Antonucci V, Andaloro L (2014) Electric vehicles charging stations network—a preliminary evaluation about Italian highways. In: 2014 IEEE international electric vehicle conference (IEVC). IEEE, pp 1–5Google Scholar
- 35.Zhenghui Z, Qingxiu H, Chun H, Xiuguang Y, Zhang D (2014) The layout optimization of charging stations for electric vehicles based on the chaos particle swarm algorithm. In: Chinese conference on pattern recognition. Springer, Berlin, pp 565–574Google Scholar
- 37.Meng W, Kai L (2017) Location of electric vehicle charging station based on spatial clustering and multi-hierarchical fuzzy evaluation. Trans Nanjing Univ Aeronaut Astronaut 1:013Google Scholar
- 38.Wei G, Wang X (2007) Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making. IEEE, Harbin, pp 495–499Google Scholar
- 39.Abdullah L, Ismail WKW (2012) Hamming distance in intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets: a comparative analysis. Adv Comput Math Appl 1(1):7–11Google Scholar
- 40.Karaşan A, Kahraman C (2017) A novel intuitionistic fuzzy DEMATEL–ANP–TOPSIS integrated methodology for freight village location selection. J Intell Fuzzy Syst 1–18 (Preprint)Google Scholar