Fuzzy decision-analytics based lithium-ion battery selection for maximizing the efficiency of electric vehicles

Abstract

The increasing reliance on fossil fuel-powered transportation significantly contributes to global warming and environmental change. Both industrial sectors and governmental bodies face challenges in addressing this issue through the adoption of green energy solutions. Electric vehicles present a more sustainable alternative due to their environmentally neutral characteristics and reduced operational costs. Batteries are integral to the functionality of electric vehicles, making the selection of an optimal battery essential. In this study, we conducted a comprehensive analysis to identify the most suitable lithium-ion batteries for the electric vehicle sector, employing fuzzy multi-criteria decision-making approaches (F-MCDM). We introduced a Complex Interval q-Rung Orthopair Fuzzy Set (CIVq-ROFS), which integrates three fuzzy sets: a complex fuzzy set, an interval-valued fuzzy set, and a q-rung orthopair fuzzy set. This integration yields more favorable outcomes compared to individual fuzzy sets. The proposed set is utilized for calculating attribute weights and ranking alternatives through the Criteria Importance Through Intercriteria Correlation (CRITIC) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method. The algorithm was tested in a case study with five alternatives and six attributes, indicating a preference for the lithium nickel cobalt aluminum oxide battery for electric vehicles. The validity of the proposed model was established by comparing it with eight F-MCDM approaches, along with Spearman rank correlation and four sensitivity analyses to evaluate their effectiveness and robustness.