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Identifying Suitable Location for Electric Vehicles Charging by Using Fermatean Fuzzy-based Personalized Decision Model

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  • Kattur Soundarapandian Ravichandran,
  • Mithun Karthik,
  • Dhruva Sundararajan
Kattur Soundarapandian Ravichandran
Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India
Mithun Karthik
Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India
Dhruva Sundararajan
Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India

Published 2026-01-25

Keywords

  • Electric vehicles,
  • Location selection,
  • Entropy measure,
  • WASPAS,
  • Fermatean fuzzy set,
  • Copeland approach
    • ...More
    Less

Copyright (c) 2026 Kattur Soundarapandian Ravichandran, Mithun Karthik, Dhruva Sundararajan (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Ravichandran, K. S., Karthik, M., & Sundararajan, D. (2026). Identifying Suitable Location for Electric Vehicles Charging by Using Fermatean Fuzzy-based Personalized Decision Model. Applied Research Advances, 2(1), 1-18. https://doi.org/10.65069/ara2120269
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Abstract

Electric vehicles (EVs) are gaining a lot of attention in the transport industry. With global leaders combating climate change and enforcing green habits, EVs are viable options for transport. One crucial challenge is charging EVs and identifying apt locations to set stations to charge EVs. The location identification problem is considered a multi-criteria decision problem (MCDP) considering diverse criteria. Previous studies on location selection for EV charging could not model uncertainty effectively, handle subjective randomness, capture experts' hesitation, and consider personalized grading of locations. Motivated by these gaps, in this paper, authors put forward an integrated framework that comprises Fermatean fuzzy preferences, entropy measure, and modified WASPAS with Copeland approach for modeling uncertainty with reduced subjective randomness, effective capturing of hesitation of experts, and obtaining both individualistic and cumulative grades for locations to charge EVs. The practicality of the framework is testified via a case example of location grading in Tamil Nadu, India. Results infer that ‘Singanallur’ and ‘Peelamedu’ are top two locations for setting charging points and criteria viz., pollution, cost, and eco-impact are relatively important for location identification. The paper presents a novel combination that methodically calculates weights and ranks locations both in the personalized and combined contexts. Its applicability to location selection is unique with the locations being considered novel for the study. The study faces following limitations viz., (i) partial information cannot be modelled, (ii) experts’ weights are not methodically obtained. and (iii) GIS-based selection process is not followed.

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