A Front Face Design of New Energy Vehicles Based on Rough Set Theory and Backpropagation Neural Network

Abstract

With the increasing awareness of environmental protection and prominent problem of traditional energy, new energy vehicles are an important choice to replace traditional oil-fueled vehicles. As an important part of new energy vehicles, the design of front face has an important impact on vehicles’ image, sales, and brand awareness. A front face’s modeling design process of new energy vehicles is proposed in this paper based on Kansei Engineering (KE)/ Rough Set Theory (RST)/ Backpropagation Neural Network (BPNN). Firstly, Kansei semantic analysis is carried out on the front face’s modeling of new energy vehicles, including collecting the front face samples of new energy vehicles and Kansei semantic words, the collected Kansei semantic words are reduced dimensional and clustered by using Factor Analysis (FA). Secondly, the morphological analysis method is used to decompose the front face samples of new energy vehicles into different design features, the attribute reduction algorithm in RST is used to identify the key design features of new energy vehicles that have an important impact on users’ satisfaction. Finally, BPNN is used to establish the mapping model between the users’ Kansei image and the key design features of new energy vehicles’ front face, thus obtaining the optimal design combination of new energy vehicles’ front face with the highest Kansei value. The results enable designers to grasp users’ sentimental cognition of new energy vehicles’ front face modeling effectively and accurately so as to improve users’ purchase desire. This method can provide references for the modeling design of related products.