The success of a new product is usually determined not by whether it includes high-end technology, but by whether it meets consumer expectations, especially key Kansei demands. This article aims to evaluate attractive factors (Kansei words) and convert them to design elements to make products stand out in the global competition. The evaluation grid method (EGM) is an important research method of Miryoku engineering. The method can build qualitative relations among consumers’ attractive factors and design elements. The quality function deployment (QFD) is a quantitative method which converts customer requirements into engineering characteristics using the House of Quality Matrix. The QFD together with the concept of fuzziness can objectively measure questionnaires made by experts. Accordingly, this paper proposes a systematic approach that integrates the EGM together with the fuzzy QFD for the development of new products. The fuzzy Kano model combined with the fuzzy analytic hierarchy process (AHP) is developed to determine the priority of the development of attractive factors. This empirical study uses minicars as an example to verify the feasibility and validity of the approach. The results are expected to help designers to increase design efficiency and improve consumer satisfaction of new products.

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Through the prevalence of sustainable ideas, automobiles are increasingly pursuing environmental protection strategies for green design, the non-traditional hybrid electric vehicles (HEV) are promoted continuously. If the company can add emotional value to the modeling of HEV, it will be helpful to its sustainable design and sales promotion of it. Therefore, an innovative model combining fuzzy linguistic preference relations (FLPR) and fuzzy quality function deployment (QFD) is proposed here to explore the connection between customer sentiment and the front view of the HEV. Compared with the previous methods, FLPR has the advantages of fewer comparison times and high consistency. First, find out the customer’s emotional expectations and attribute weight ranking for HEV through FLPR, and import customer requirements (CRs) on the left side of fuzzy QFD. Second, the grey prediction model was used to screen out the key engineering features (ECs) and the initial weight of HEV. Finally, based on human subjective imprecise natural semantics, fuzzy QFD established a matrix association between CRs and key ECs, then finally obtained the optimal combination of ECs' final weight and morphological design. The results can assist designers to shorten product development cycles and improve customers' emotional satisfaction, which provides a theoretical reference for the sustainable design and marketing of environmentally friendly cars in the future.