Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

Zongming Liu, Xuhui Chen (), Xinan Liang, Shiwen Huang and Yang Zhao
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Zongming Liu: School of Design and Art, Shaanxi University of Science and Technology, Xi’an 710016, China
Xuhui Chen: School of Design and Art, Shaanxi University of Science and Technology, Xi’an 710016, China
Xinan Liang: School of Packaging Design and Art, Hunan University of Technology, Zhuzhou 412007, China
Shiwen Huang: School of Architecture and Design, Nanchang University, Nanchang 330031, China
Yang Zhao: School of Design and Art, Shaanxi University of Science and Technology, Xi’an 710016, China

Sustainability, 2024, vol. 16, issue 17, 1-22

Abstract: With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

Keywords: sustainable development; pure electric vehicles; particle swarm optimization support vector regression; perceptual engineering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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