A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective

An integrated multi-criteria decision-making (MCDM) method is developed through the linguistic entropy weight (LEW) method and fuzzy axiomatic design (FAD) to select a suitable site for an electric-vehicle charging station (EVCS). Based on expert opinions from different fields, a literature survey, and on-site investigation, an evaluation index system for EVCS site selection is constructed from a sustainable perspective; the indicator system has 13 sub-criteria, including technical, economic, social, environmental, and resource ones. Next, outcomes are presented from the criteria performances and weights of different alternatives having been evaluated by a panel of five technical, economic, social, ecological, and urban-planning experts. Finally, criteria weights are determined by the LEW method, and the most suitable EVCS site is determined by the FAD method. Moreover, a LEW–FAD integrated analysis framework is constructed, and the process for calculating the optimal EVCS location is given. To assess the stability and robustness of the proposed method, sensitivity and comparative analyses are conducted. The results of the sensitivity analysis show that the ranking of alternatives is unaffected by changes in the functional requirements of the criteria but affected considerably by changes in the criteria weights. The advantages of the proposed method are highlighted in terms of stability and reliability by comparisons with three MCDM methods applied in previous studies, and the effectiveness of the proposed method is verified. The results show that the application of LEW method and FAD in EVCS site selection is robust. Therefore, the evaluation criteria and method proposed in this paper are also suitable for other rapidly developing or emerging economies.

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  • English

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  • Accession Number: 01761498
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 3 2020 3:21PM