A Fuzzy Decision-Making Approach to Electric Vehicle Evaluation and Ranking

Abstract

One of the key challenges facing the modern automotive industry is aligning customer demands with increasingly stringent environmental regulations and policies. In addition to introducing various modifications and improvements to internal combustion engine vehicles, manufacturers are increasingly focusing on the development of electric and hybrid vehicles. Moreover, the goal is to ensure that the product remains both cost-effective for manufacturers and sufficiently affordable for potential customers. This study addresses the problem of selecting electric vehicles based on features that are significant from both the customer and manufacturer perspectives. The objective of this research is to evaluate and rank electric vehicle models from various global manufacturers based on features collected and integrated from diverse literature sources. A total of 18 electric vehicle models were analysed and assessed according to 10 features. This research introduces a fuzzy multi-criteria decision-making model for mathematically determining the rank of electric vehicles. The obtained results serve as input data for benchmarking and optimizing business strategies in automotive companies, which represents the practical contribution of this study. The evaluation of the relative importance of features is framed as a fuzzy group decision-making problem, where decision-makers express their assessments using predefined linguistic terms modelled with triangular fuzzy numbers. The ranking of the analysed electric vehicles is conducted using modified fuzzy TOPSIS and fuzzy COPRAS methods, which constitute the main scientific contribution of this research. A methodology for ranking similarity comparison was applied to examine the consistency of the results obtained through these two methods.