Advancing data-driven sustainable design: A novel NEV form design approach in China's market

As the concept of sustainable development spreads globally, New Energy Vehicles (NEVs) have increasingly become a focal point in social and environmental agendas. In the form design process of NEVs, transforming the design workflow from a traditional resource and time-consuming model to a rapid and efficient intelligent design, along with the objective and precise extraction of user needs and engineering analysis, represents a critical systemic task. Utilizing online review data, the research employs natural language and image processing to establish a cross-modal generation model that aligns image schemes with user expectations. Further, a deep convolutional neural network for Kansei label recognition refines selections, while multi-criteria compromise ranking and computational fluid dynamics simulations ensure the designs' sustainability and aerodynamic efficiency. Guided by big data insights, the resultant NEV designs advance sustainable consumption, energy efficiency, and innovative solutions to environmental challenges, highlighting the study's contribution to sustainable automotive development.