Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

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.

The influence of temperature on the damping characteristic of hydraulic shock absorbers

“…In previous scientific research, shock absorber tests results could be found that take into account temperature [5,10,14,18] and their impact on the driving comfort of the user [13]. Studies of the shock absorbers filled with numerous types of fluids were also presented in [12], where the authors presented their heating characteristics during operation with a constant amplitude and frequency.…”

Study on cavitation phenomenon of twin-tube hydraulic shock absorber based on CFD

“…Due to high demanding for comfort of automobiles, the suitable design of the hydraulic shock absorber becomes very important (Faraj, Holnickiszulc, & Knap, 2016). Hydraulic shock absorber is an important part of vehicle suspension and steering system (Preda, 2016), it reduces the vibration between the wheel and the car main body through the damping force generated by the internal throttle slice (Yu, Ye, & Wang, 2011), and it can also alleviate the vibration and impact of the car main body caused by uneven road surface, which can improve comfort of the vehicle ride (Pracny, Meywerk, & Lion, 2008). However, the hydraulic shock absorber will be aged if it works for a long time in an environment with strong vibration impact and large temperature change (Dong, Yu, & Yang, 2016) and the inner part of the hydraulic shock absorber will circulate to generate high-pressure and low-pressure area.…”

A fast and accurate hybrid simulation model for the large-scale testing of automated driving functions

“…[11][12][13] In Holzmann et al, 14 they used a radial basis function network to compensate influences of changing road conditions on parameters of an analytic simulation model. Moreover, Pracny et al 15,16 developed a shock absorber model based on the coupling of a neural network and a spline function. Next to its accurate modelling capabilities as shown in the above-mentioned areas, hybrid models require fewer training samples which make them easier to adapt to parametric changes.…”