A Method for the Front-End Design of Electric SUVs Integrating Kansei Engineering and the Seagull Optimization Algorithm
Year: 2025
Type: article
Abstract: With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering (KE) approaches suffer from limited timeliness, subjectivity, and low predictive accuracy when extracting affective vocabulary and modeling the nonlinear relationship between product form and Kansei imagery. To address these challenges, this study proposes an improved KE-based ESUV styling framework that integrates data mining, machine learning, and generative AI. First, real consumer reviews and front-end styling samples are collected via Python-based web scraping. Next, the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) are used to extract representative Kansei vocabulary. Subsequently, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models are constructed and optimized using the Seagull Optimization Algorithm (SOA) and Particle Swarm Optimization (PSO). Experimental results show that SOA-BPNN achieves superior predictive accuracy. Finally, Stable Diffusion is applied to generate ESUV design schemes, and the optimal model is employed to evaluate their Kansei imagery. The proposed framework offers a systematic and data-driven approach for predicting consumer affective responses in the conceptual styling stage, effectively addressing the limitations of conventional experience-based design. Thus, this study offers both methodological innovation and practical guidance for integrating affective modeling into ESUV styling design.
Source: Electronics
Authors: Yutong Zhang, Jiantao Wu, Li Sun, Qi Wang, Xiaotong Wang, Yiming Li
Institution: Yanshan University
Language: English
Topic: Color perception and design
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409569123