Attractive form design of new energy vehicle based on interval Type-2 Trapezoidal Fuzzy Kano Model and HO-XGBoost

In the increasingly competitive New Energy Vehicle (NEV) market, consumers’ purchase decisions are influenced not only by performance but also by emotional responses evoked by vehicle appearance. However, existing studies lack a robust framework to translate such preferences into concrete design parameters and to resolve the mapping between vague emotional needs and design features. To address this gap, this paper proposes an attractive NEV form design approach integrating the Interval Type-2 Trapezoidal Fuzzy Sets-Kano Model (IT2Tr-FKM) with the Hippopotamus Optimisation Algorithm-eXtreme Gradient Boosting (HO-XGBoost). Based on a three-level evaluation structure constructed using the Evaluation Grid Method (EGM) of Miryoku Engineering, IT2Tr-FKM is used to evaluate and prioritise upper-level Kansei words. Morphological decomposition then links key Kansei words to abstract reasons and concrete design attributes. HO-XGBoost establishes a mapping between Kansei words and representative design features to identify high emotional appeal solutions. Eye-tracking and the Data Information Difference Fluctuation Weighting Method (DIDF) are further applied for objective and subjective evaluation, ultimately selecting the optimal NEV form design. The proposed framework improves design efficiency and user satisfaction.