Emotional mapping of electric vehicle front face features using the KE-EAS framework

This study proposes a three-dimensional feature decomposition method integrating user emotional cognition to address the lack of systematic approaches in electric vehicle (EV) front face design. Based on Norman's three-level theory and Kansei Engineering, we constructed the KE-EAS framework (Kansei Engineering-Exterior/Affordance/Symbolize). Using EV front face design as a case study, the framework was validated through expert analysis, user categorization, eye-tracking, and factor analysis. By integrating AHP with eye-tracking data, we established a feature weighting system and developed an emotional mapping model. Results demonstrated high expert-user consistency in feature recognition and a clear alignment between model predictions and user evaluations. The KE-EAS framework effectively identifies design features driving emotional responses, while the mapping model establishes quantitative feature-preference correlations. This research provides both theoretical tools and practical references for EV front face design, supporting market adoption through enhanced emotional acceptance and contributing to sustainable transportation development.