Gas struts are widely used in vehicle suspensions. However, they are highly non-linear and conventional linear models are not sufficient to describe their complex behaviour. In this paper, two non-linear gas strut models with different modelling approaches are presented. One is based on grey-box methods using the modular dynamic simulation package Bathfp, whrer both physical parameters and experimental data are used to model the strut. The other uses black-box methods in Matlab, where artificial neural networks are employed and experimental data are used to train the model. Simulation studies demonstrate that the neural network model is more suitable for real vehicle simulations, whereas the Bathfp model is more appropriate for detailed system dynamics analysis, especially isos-frequency studies. Since the Bathfp model is able to predict the strut performance when certain parameters in the system are changed and can be easility integrated into different vehicle models, it is a useful design tool for both strut manufacturers and automotive original equipment manufacturers.
|Number of pages||25|
|Journal||Transactions of the Institute of Measurement and Control|
|Publication status||Published - 2007|