No abstract

In the case of full vehicle models, the technique of multi-body simulation (MBS) is frequently used to study their highly non-linear dynamic behaviour. Many non-linearities in vehicle models are induced by force elements like springs, shock absorbers, bushings and tires. Commonly, spline functions are used to represent the force responses of these components. If the non-linear relationships are more complicated, the spline approximations are no more accurate. An alternative approach is based on empirical neural networks which are based on the mathematical approximation of measured data. It is well known that neural networks are able to represent and predict complex component responses accurately. The aim of this paper is to perform a dynamic full vehicle simulation using a thermomechanically coupled hybrid neural network shock absorber model. In this shock absorber model, the spline approach is combined with a temperature-dependent neural network. Based on a displacement-controlled excitation on a four post test rig in the ADAMS/Car MBS software, a rugged test track is simulated. In this way, the front and rear shock absorbers are dynamically loaded with comfort-relevant frequencies in the range of 0.75-30 Hz and velocity amplitudes up to 2 m/s. By the simulation, stability of the hybrid neural network model is demonstrated. Furthermore, the damping force, the vertical acceleration of the chassis and the required simulation times are compared. The standard spline approach is used as a reference.

A hybrid neural network model is presented and described. The model is composed of a mechanical and thermodynamical part. The mechanical part is described by Akima spline in combination with a feed-forward neural network, while in the case of the thermodynamic part differential equation of dissipative heating is formulated. The interface between both part is provided by the neural network. To identify a proper parameter set of the hybrid model a shock absorber of a middle class passenger car is measured on a servo-hydraulic testing machine. As an excitation a stochastic signal with a predefined power spectral density (PSD) is used. Subsequently the hybrid shock absorber is implemented into a full vehicle model in ADAMS/Car to test its numerical performance and influence on the vertical vehicle dynamics. As reference a standard spline shock absorber model is taken.