Multi-scale Parametric Optimization of CFRP Electric Vehicle Occupant Compartment Structures

Confronting the dual challenges of depleting petroleum resources and stringent emission regulations, the development of electric vehicles (EVs) has become an imperative for the automotive industry. However, a significant research gap remains in the structural design of occupant compartments for all-new EV platforms. Furthermore, the multi-scale design complexities of Carbon Fiber Reinforced Polymer (CFRP) pose critical technical bottlenecks hindering its large-scale application in vehicle bodies. Originating from a National Key R&D Program project undertaken by the author's team, this book focuses on the CFRP occupant compartment structure for a new-architecture EV. It presents an in-depth journey of design and optimization, spanning from "macro-scale architecture" to "micro-scale ply layup." The research pioneers an equivalent single-layer modeling method for large, complex integrated CFRP structures, overcomes technical hurdles in the parametric design of variable-thickness layups and cross-category material selection co-optimization, and establishes a comprehensive "macro-micro" multi-scale fully parametric modeling and integrated optimization design framework. By integrating contribution analysis, surrogate models, multi-objective optimization algorithms, and multi-criteria decision-making methods, the final design scheme meets all performance targets while achieving remarkable lightweighting and cost reduction. The prototyping of representative components and validation by third-party accredited testing institutions robustly confirm the effectiveness and engineering practicality of the proposed methodologies. This book provides a systematic methodology and a validated technical pathway for the innovative design of EV body structures, particularly CFRP components. It serves as a valuable reference for researchers and engineers specializing in automotive lightweighting, composite structural design, and multidisciplinary optimization.