A hybrid modeling approach for automotive vibration isolation mounts and shock absorbers

This paper presents a novel modeling approach to predict the nonlinear dynamic characteristics of automotive mounts and shock absorbers. Firstly, the concept of the hybrid ANN (artificial neural network)–mechanical modeling approach is presented, which consists of an equivalent mechanical model to characterize the trend of the dynamic characteristic and a neural network model to compensate for the errors introduced by uncaptured nonlinearities. Then experiments are carried out on a rubber mount, a hydraulic mount and a shock absorber to measure their static and dynamic characteristics. Parameters of the equivalent mechanical model are identified, and the neural network model is trained. The hybrid models are validated under harmonic and random excitations with a relative error of less than 8%. Finally, the developed models for the rubber mounts, hydraulic mounts and shock absorbers are integrated into a vehicle model to evaluate the impact of nonlinearity on vehicle ride comfort. The results show that ignoring the nonlinearity of the hydraulic mounts will introduce the largest calculation errors in the analysis of vehicle ride comfort, followed by the shock absorber, and the rubber mount is the smallest.