Skip to main content

Advertisement

SpringerLink
Log in

Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Eisel M, Schmidt J, Kolbe LM (2014) Finding suitable locations for charging stations. In: 2014 IEEE international electric vehicle conference (IEVC). IEEE, pp 1–8

  2. Traut E, Hendrickson C, Klampfl E, Liu Y, Michalek JJ (2012) Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost. Energy Policy 51:524–534

    Google Scholar 

  3. Islam MM, Shareef H, Mohamed A (2015) Optimal quick charging station placement for electric vehicles. Appl Mech Mate 785:697–701

    Google Scholar 

  4. Lam AY, Leung YW, Chu X (2014) Electric vehicle charging station placement: formulation, complexity, and solutions. IEEE Trans Smart Grid 5(6):2846–2856

    Google 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 150–156

  6. Liu Z, Wen F, Ledwich G (2013) Optimal planning of electric-vehicle charging stations in distribution systems. IEEE Trans Power Deliv 28(1):102–110

    Google Scholar 

  7. Kong C, Jovanovic R, Bayram IS, Devetsikiotis M (2017) A hierarchical optimization model for a network of electric vehicle charging stations. Energies 10(5):675

    Google 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 4:59–65

    Google 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 1–6

  10. Tabari M, Kaboli A, Aryanezhad MB, Shahanaghi K, Siadat A (2008) A new method for location selection: a hybrid analysis. Appl Math Comput 206(2):598–606

    MathSciNet  MATH  Google Scholar 

  11. Zadeh LA (1965) Fuzzy set. Inf Control 8:338–353

    MATH  Google 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):1–28

    Google Scholar 

  13. Kaya İ, Erdoğan M, Yıldız C (2017) Analysis and control of variability by using fuzzy individual control charts. Appl Soft Comput 51:370–381

    Google Scholar 

  14. Kaya İ (2014) The process incapability index under fuzziness with an application for decision making. Int J Comput Intell Syst 7(1):114–128

    Google Scholar 

  15. Kaya İ (2012) Evaluation of outsourcing alternatives under fuzzy environment for waste management. Resour Conserv Recycl 60:107–118

    Google Scholar 

  16. Özkan B, Kaya I, Başligil H (2017) A fuzzy based goal programming methodology for minimizing the risk factors: a real case application in pharmaceutical sector. J Multip Valued Logic Soft Comput 28(4–5):475–493

    MathSciNet  Google Scholar 

  17. Parchami A, Ivani R, Mashinchi M, Kaya İ (2017) An implication of fuzzy ANOVA: metal uptake and transport by corn grown on a contaminated soil. Chemometr Intell Lab Syst 164:56–63

    Google 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 832–834

  19. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    MATH  Google Scholar 

  20. Zhao T, Xiao J (2012) Type-2 intuitionistic fuzzy sets. Control Theory Appl 29(9):1215–1222

    Google 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 1–8

    Google 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 Eng

  23. Erdogan M, Kaya I (2015) An integrated multi-criteria decision-making methodology based on type-2 fuzzy sets for selection among energy alternatives in Turkey. Iran J Fuzzy Syst 12(1):1–25

    MathSciNet  Google Scholar 

  24. Wang Y-W, Lin C-C (2009) Locating road-vehicle refueling stations. Transp Res Part E 45:821–829

    Google 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 173–178

  26. Schill WP (2011) Electric vehicles in imperfect electricity markets: the case of Germany. Energy Policy 39(10):6178–6189

    Google Scholar 

  27. Kley F, Lerch C, Dallinger D (2011) New business models for electric cars—a holistic approach. Energy Policy 39(6):3392–3403

    Google 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 12:391–416

    Google 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 conference

  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 90–95

  31. He F, Wu D, Yin Y, Guan Y (2013) Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Transp Res Part B Methodol 47:87–101

    Google 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 510–515

  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), Milan

  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 1–5

  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 565–574

  36. Sadeghi-Barzani P, Rajabi-Ghahnavieh A, Kazemi-Karegar H (2014) Optimal fast charging station placing and sizing. Appl Energy 125:289–299

    Google 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 1:013

    Google 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 495–499

  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):7–11

    Google 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)

  41. Kwong CK, Bai H (2003) Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Trans 35(7):619–626

    Google Scholar 

  42. Lee AH, Chen WC, Chang CJ (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34(1):96–107

    Google Scholar 

  43. Wang L, Chu J, Wu J (2007) Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107(1):151–163

    Google Scholar 

  44. Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci 170(2–4):191–210

    MATH  Google Scholar 

  45. Kwong CK, Bai H (2002) A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J Intell Manuf 13(5):367–377

    Google Scholar 

  46. Dong Y, Zhang G, Hong WC, Yu S (2013) Linguistic computational model based on 2-tuples and intervals. IEEE Trans Fuzzy Syst 21(6):1006–1018

    Google Scholar 

  47. Dong Y, Xu Y, Yu S (2009) Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Trans Fuzzy Syst 17(6):1366–1378

    Google Scholar 

  48. Herrera-Viedma E, López-Herrera AG (2007) A model of an information retrieval system with unbalanced fuzzy linguistic information. Int J Intell Syst 22(11):1197–1214

    MATH  Google Scholar 

  49. Herrera F, Herrera-Viedma E, Martínez L (2008) A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 16(2):354–370

    Google Scholar 

  50. Li CC, Dong Y, Herrera F, Herrera-Viedma E, Martínez L (2017) Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Inf Fusion 33:29–40

    Google Scholar 

  51. Pedrycz W, Song M (2014) A granulation of linguistic information in AHP decision-making problems. Inf Fusion 17:93–101

    Google Scholar 

  52. Wu J, Huang HB, Cao QW (2013) Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Appl Math Model 37(24):9898–9906

    MathSciNet  MATH  Google Scholar 

  53. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  54. Park JH, Park IY, Kwun YC, Tan X (2011) Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Appl Math Model 35(5):2544–2556

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Natural and Applied Sciences, Yildiz Technical University, 34220, Davutpaşa, Istanbul, Turkey

    Ali Karaşan

  2. Department of Industrial Engineering, Yıldız Technical University, 34349, Yıldız, Beşiktaş, Istanbul, Turkey

    İhsan Kaya & Melike Erdoğan

Authors
  1. Ali Karaşan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. İhsan Kaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Melike Erdoğan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to İhsan Kaya.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karaşan, A., Kaya, İ. & Erdoğan, M. Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey. Neural Comput & Applic 32, 4553–4574 (2020). https://doi.org/10.1007/s00521-018-3752-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3752-2

Keywords

Search

Navigation