Improved intelligent decision model for electric vehicle battery using q-Rung orthopair fuzzy Schweizer–Sklar prioritized Z-information

Abstract The growing interest in the electric vehicles (EVs) has highlighted the significance of choosing appropriate battery technologies according to various competing and unpredictable norms. In the development of EV the selection of appropriate battery is a crucial step, as this component directly affects the cost, performance and ecological system. This task often includes technical, economic and ecological system which are directly incorporated with uncertainty, ambiguity and imprecision. In order to enhance the decision making process of these complex environments, the current study presents a new category of Schweizer-Sklar based prioritized aggregation operators under q-Rung Orthopair Fuzzy Z-Numbers (qROFZNs) framework. These operators are particularly constructed to have prioritization between the attributes and are well prepared to address the increased levels of uncertainty in expert judgments. In order to confirm the efficacy of the suggested strategy, the overall case study of EV battery options is carried out. The findings indicate the way the designed operators increase the ranking stability, reliability, and flexibility in the selection. The results reveal that the suggested operators outperform existing fuzzy MCDM approaches in terms of stability and reliability, as well as robustness to parameter fluctuations. In the name of comparative analysis, the WASPAS model is implemented in the qROFZ context with the first time and some characteristic and analytical comparisons are made between the given model and the available fuzzy MCDM methods. These reviews prove that the developed approach is able to guarantee not only the consistency of the obtained decision but also outcompete the conventional models in regard to their reliability, strength, and flexibility. The results indicate that the proposed framework is a more potent and versatile instrument to use by decision-makers, who have to address uncertainty, and it can be easily extended to other complicated sustainability-related decision-making issues, including EV battery selection.