Signless Laplacian energy aware decision making for electric car batteries based on intuitionistic fuzzy graphs.

Abstract

Fuzzy graphs (FGs) contain dual-nature characteristics that may be extended to intuitionistic fuzzy graphs. These FGs are better at capturing ambiguity in situations in reality involving decision-making than FGs. In this paper, we address decision-making problems based on intuitionistic fuzzy preference relations (IFPRs) by utilizing Signless Laplacian energy (S_LE), intuitionistic fuzzy weighted averaging (IFWA), and intuitionistic fuzzy weighted averaging geometric (IFWAG). The paper suggests an approach that makes use of intuitionistic fuzzy graphs (IFG) and IFPR to optimize batteries for electric vehicles. Electric vehicles (EVs) performance, range, and efficiency are all dependent on battery technology. Research and technological developments may help remove adoption hurdles and increase public interest in EVs. Producers of batteries and automakers are investing in recycling and cost-cutting measures for manufacture. With the use of carbon nanotube electrodes, battery power may be increased tenfold beyond existing capabilities. In a procedure called group decision-making, experts evaluate and choose the best options based on present standards. This method provides crucial data for well-informed decision-making by capturing ambiguity and uncertainty in real-world decision-making. The strategy improves decision-making and maximizes profits, giving investors a useful foundation for choosing environmentally friendly electric vehicle batteries.