Conceptual Structural Optimization and AI-Based Stress Prediction of a Monocoque Chassis for Formula Student Electric Vehicle

This study presents the conceptual design, optimization, smart material assessment, and ai based stress prediction of the monocoque chassis of the Formula Student Electric Vehicle (FSEV). The chassis designed according to the 2025 Formula Student guidelines focusing on high structural compliance and safety, stiffness, and minimal weight [1],[2]. A total of 14 engineering materials were compared and analysed structurally for their performance in regards to impacts. The material selection was done based on the finite element analysis (FEA) simulations of front and rear impacts concerning total deformation. Only the most favourable material was selected based on the FEA simulations of the front and rear impacts [3],[4]. A detailed computer-aided design (CAD) model created on SolidWorks, and ANSYS Workbench was used to conduct the FEA for front, rear, and side impacts. Through iterative topology optimization, unnecessary materials were eliminated to the maximum degree while ensuring that the structure remained crashworthy. The result of the optimization process on the CFRP monocoque is a weight reduction of 35–40% while ensuring that high stiffness and safety requirements are met compared to the conventional spaceframe. An AI regression model for stress prediction has been created as a method for increasing efficiency. Using Young’s modulus, yield strength, and Poisson’s ratio as inputs, the model has been shown to reduce the number of simulations needed for stress distribution predicting and accelerate the process of selecting materials. The combination of AI and FEA has created a solid, time-saving approach to the design of chassis. The research suggests that AI modelling and CFRP monocoques provide next generation Formula Student electric vehicles with improved safety, better efficiency, and cost-effective alternatives to competitors with high stiffness-to-weight ratios [2].