A Methodology to Integrate a Nonlinear Shock Absorber Dynamics into a Vehicle Model for System Identification 2011-01-0435

High fidelity mathematical vehicle models that can accurately capture the dynamics of car suspension system are critical in vehicle dynamics studies. System identification techniques can be employed to determine model type, order and parameters. Such techniques are well developed and usually used on linear models. Unfortunately, shock absorbers have nonlinear characteristics that are non-negligible, especially with regard the vehicle's vertical dynamics. In order to effectively employ system identification techniques on a vehicle, a nonlinear mathematical shock absorber model must be developed and then coupled to the linear vehicle model. Such an approach addresses the nonlinear nature of the shock absorber for system identification purposes. This paper presents an approach to integrate the nonlinear shock absorber model into the vehicle model for system identification.

Three empirical mathematical models are proposed that describe the relationship between shock velocity and shock force. These models are used to predict the force behavior of the shock absorber when the shock absorber velocities ranged from 0 to 1.5m/s, in both compression and extension direction. Empirical shock absorber models can be quickly developed using experimental data and integrated into the larger vehicle model for complete system identification. The shock velocity calculated from the vehicle model is the input to the shock absorber model and the shock force is the feedback to the vehicle model. The influence of nonlinear shock absorber models on system identification for the half car model was analyzed. The result shows that the type of shock absorber model did not have much influence on vehicle system identification using simulated data generated from the model. Furthermore, the system identification method, integrating the nonlinear shock absorber dynamics model into the vehicle model, works well for the simulated data. Future work will focus on the use of experimental data.