Rough Road Vehicle Dynamics Analysis for Vehicle Vibration Assessment

This study is to demonstrate a vehicle dynamics simulation process to assess vehicle vibration performance. A vehicle dynamics model including non-linear tuning elements and flexible vehicle body is simulated on ride roads. The goal of the simulation is acceleration responses at the passenger locations in frequency domain. Body interface loads are recovered from the vehicle dynamic simulations. Frequency response function (FRF) of the body structure is ready in a fashion that input forces are applied to all body interface locations to the suspension and powertrains. This will give acceleration response sensitivity of the body structure to each body interface. The sum of body interface loads multiplied by FRF at each interface produces acceleration responses in frequency domain. A mid-size sedan model was used to demonstrate the process. A full vehicle dynamics model using Ansys Motion was simulated on a virtual ride road at a constant speed. The body loads were recovered in time domain and Fast Fourier Transformed (FFT) to convert them in frequency domain. The Body-In-white model was run a harmonic analysis to get body FRF. Python code was used to automate data communication and acceleration response calculation. The vehicle acceleration performance at the passenger locations is plotted in frequency domain. This process successfully demonstrated vehicle vibration performance at driver positions in terms of suspension tuning. It can make several design recommendations not available in the 100% vehicle dynamics approach, where the accelerations from the passenger locations are obtained from the same vehicle dynamics simulation. Not mentioning acceleration performance in frequency domain, it can pinpoint contributions of each load path from body interface points to the passenger locations. The contribution from each load path enables engineers to explore the design compromises: the most cost effective or the most timing effective design changes or both.