OPTIMIZING SHEET MOLDING COMPOSITES-BASED ELECTRIC VEHICLE BATTERY COVERS FOR PERFORMANCE AND MANUFACTURABILITY

Simulation-driven design plays a pivotal role in advancing lightweight and high-performance components for electric vehicles (EVs). This study presents a comprehensive, manufacturing-informed simulation framework for the design, evaluation, and optimization of a Sheet Molding Compound (SMC)-based EV battery cover. SMCs offer notable advantages over metals such as aluminum, including lower density, cost-effectiveness, and excellent moldability. However, their performance is highly sensitive to processing conditions and material anisotropy, necessitating integrated modeling strategies that account for manufacturing variability. The proposed multiscale workflow incorporates compression molding simulations to capture fiber orientation tensors, micromechanical modeling to represent anisotropic behavior, and structural analysis for performance assessment. Comparative analysis demonstrates that, while aluminum offers higher intrinsic stiffness, optimized SMC designs can achieve equivalent or superior performance with a 36–40% weight reduction. To match the performance of aluminum, the SMC part geometry was strategically modified through targeted additions such as ribs, fillets, and increased thickness. Additionally, several charge patterns were evaluated to identify configurations that enhance fiber alignment and reduce critical failure regions. A design of experiments (DoE) study was performed to evaluate the influence of key process parameters, including charge pattern, placement, and thickness, on structural performance. A reduced-order model (ROM) was developed to enable efficient uncertainty quantification (UQ) by propagating variations in fiber orientation and axial tensile strength. Reliability analysis revealed an 81% likelihood of meeting performance criteria, with the second tensor component of fiber orientation identified as the most influential variable. The findings highlight the value of anisotropic modeling and process-aware optimization in achieving lightweight, reliable, and structurally robust composite components. This framework lays the groundwork for future automation of process parameter optimization for enhanced manufacturability and product consistency.