In these days, there is a paradigm shift from internal-combustion-engine vehicles to electric vehicles. Most electric vehicles developed include batteries mounted at the bottom, near the rear wheels. Hence, the robust design of underbody parts against the impact of external bodies or random stone chipping needs to be made. In this study, the mathematical modeling and statistical probability analysis of stone chipping and tire slip are performed for identifying and confirming the critical zones of the vehicle underbody that may be damaged by stone chipping. Thereby, stone chipping can be predicted by simulations using the employed mathematical model, before conducting experimental verification using the existing methods. Furthermore, the development cost and time can be reduced because the elements of the designed underbody can be analyzed for robustness, and the safety factor can be established during the design stage.

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Computer-aided engineering (CAE) tools play an indispensable role in the vehicle development process. However, it is difficult to accurately predict the relationships and behavior of automotive bodies in vehicle crashes owing to high-order nonlinearity and numerous design variables of the automotive body structure. In this study, clustering and pattern recognition techniques were used to develop a novel optimization design of an automotive body considering roof crushing by vehicle rollover. The large-scale data were clustered to find the strong and weak clusters, and new response surface models were acquired by clustering analysis to achieve better performance than the response surface model of traditional optimization. For an efficient robust design, clusters with weak performance were excluded from the optimum solution. Finally, it was confirmed that the solutions by the proposed optimization technique were better than those obtained by the traditional optimum method based on a comparative analysis by various cluster combinations.