Optimization Study of Driver Crash Injuries Considering the Body NVH Performance

Abstract

Optimal body structure design is a central focus in the field of passive automotive safety. A well-designed body structure enhances the lower threshold for crash safety, serving as a basis for the deployment of other safety systems. Frontal crashes, particularly those with an overlap rate below 25%, are the most frequent types of vehicular accidents and pose elevated risks to occupants due to variable energy absorption and force transmission mechanisms. This study aims to identify an optimized, cost-effective, and lightweight solution that minimizes occupant injuries. Using a micro-vehicle as a case study and accounting for noise, vibration, and harshness (NVH) performance, this paper employs Elman neural networks to predict key variables such as the first-order modes of the body, the body’s mass, and the head injury values for the driver. Guided by these predictions and constrained by the first-order modes and body mass, a genetic algorithm was applied to explore optimal solutions within the solution space defined by the body panel thickness. The optimized design yielded a reduction of approximately 173.43 in the driver’s head injury value while also enhancing the noise, vibration, and harshness performance of the vehicle body. This approach offers a methodological framework for future research into the multidisciplinary optimization of automotive body structures.