Combining rough set theory and support vector regression to the sustainable form design of hybrid electric vehicle

摘要

With the popularity of cleaner production and sustainable concepts, hybrid electric vehicle (HEV) has become the most popular alternative after fuel vehicles. Hybrid electric technology reduces air pollution and makes up for the shortcomings of battery life. When customers purchase HEVs, except for considering mechanical properties, Kansei consensus has become a key factor influencing communication between manufacturers and customers. Therefore, the purpose of this paper is to explore the mapping relationship between customer visual sensibility and HEV shape design based on Kansei engineering. First of all, the morphological analysis method deconstructs the appearance of HEV and establishes a Likert scale between it and representative perceptual appeal. Secondly, the attribute reduction algorithm in rough set theory is used to identify the key HEV patterns that have an important impact on customer satisfaction. Finally, support vector regression is used to establish a mapping model between customer Kansei and key morphological characteristics of HEV, and the optimal product design combined with the highest Kansei value is obtained. Taking modern emotional as an example, the mapping relationship between product design features and Kansei quality is established. It is found that the optimal emotion of customers could be obtained by selecting hub type seven, side windows type six, grill type three, front doors type three, and headlights type eight in the morphological deconstruction table. The research results enable designers to accurately grasp the customers & rsquo; emotional cognition of HEV styling and reinterpret the future HEV body styling, thereby improving customers & rsquo; purchase desire and satisfaction.(c) 2021 Elsevier Ltd. All rights reserved.