Evaluating vehicle architecture alternatives under uncertainty: a spherical fuzzy MCDM framework

English (OpenAlex)

Weight, fuel economy, control complexity, cost, powertrain efficiency, comfort, and emissions are some of the factors considered in the design of vehicle architecture. The global actions to reduce greenhouse gas emissions have accelerated the transformation of vehicle architecture. In this situation, electric vehicles (EVs) have emerged as alternatives because of their low emissions and increased energy efficiency. Also, recent advancements in powertrain systems, battery technologies, and charging infrastructure have reinforced the structural advantages of EVs compared to traditional internal combustion engine (ICE) vehicles. However, the factors influencing architectural design choices vary significantly depending on the energy source, creating considerable uncertainty in the decision-making process. Although many fuzzy multi-criteria decision-making (MCDM) methods have been used to evaluate vehicles, spherical fuzzy sets (SFS) which are even better at handling uncertainty and imprecision, haven't been widely adopted yet. This framework makes it easier to thoroughly and realistically compare different vehicle types, including Conventional, Electric, Series Hybrid, Parallel Hybrid, Series-Parallel Hybrid, and Complex Hybrid architectures by considering not only technical factors, but also economic and environmental aspects. The results reveal that Series Hybrid, Electric, and Conventional architectures rank as the top three alternatives, achieving overall scores of 3.418, 2.82, and 2.267, respectively. Series Hybrid architecture significantly outperforms lower-ranked configurations such as Parallel Hybrid (0.068) and Series-Parallel Hybrid (0.077), indicating a strong discriminative capability of the proposed SF framework. The integration of SFS provides a reliable decision support tool for sustainable and efficient vehicle design.