Efficient Empirical Modeling of a High-Performance Shock Absorber for Vehicle Dynamics Studies

Race teams frequently use tools like shock dynamometers (dynos) to characterize the complex behavior of shock absorbers after they are built and before they are put on the race car for testing to make sure they perform as expected. One way to make use of this shock dyno data is to use it to create a model to predict shock absorber performance over a wide range of inputs. These shock models can then be integrated into vehicle simulations to predict how the vehicle will respond to different shock selections, and aid the race engineer to narrow down possible shock setups before track testing.

This paper develops an intuitive nonlinear dynamic shock absorber model that can be quickly fit to experimental data and implemented in simulation studies. Unlike other existing dynamic race shock models, it does not suffer from the complexity of modeling complex physical behavior, or the inefficiencies of unstructured black-box modeling. The model consists of a static backbone, which is a function of velocity alone, and a nonlinear low-pass filter, which has been designed based on the observation that the damper can respond more quickly at higher velocities. Due to the simplicity of the model, it can be fitted with data and run quickly. The model was fitted using shock dyno data from a random input. The completed model is then validated against other random data and sine wave data over a wide range of amplitudes and frequencies. This analysis shows the strengths and weaknesses of the model, and suggests areas for future development.