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Vertical excitations from obstacles on public road are typical and likely to increase vehicle interior vibration through major paths of wheel spindle-suspension-body. A new transient transfer path analysis (TTPA) methodology is presented combining the substructure reverse matrix method based on FRFs with operational excitation. Additionally, a new kind of experimental method is applied to solve an engineering problem and also validates the TTPA theory above. There are three steps in all. Firstly, vibration in Z direction of wheel spindle was collected in one proving ground and represented on MTS 320 road simulator bench after many times of iteration of piston signals. This procedure guarantees excitation decoupling in one certain direction so it leads to accurate frequency response functions (FRFs) under transient shocking excitation. Secondly, the new transient transfer path analysis approach was used to calculate vibration contribution of wheel-suspension-body.

A series connection of the KVBC (Kelvin-Voigt and Bouc-wen) theoretical model of rubber bush in automobile suspension is established. The numerical calculation model is also developed through Matlab/simulation and 9 parameters are identified. Experiments are conducted on the rubber bush on a bench for dynamic and static characteristics and to supply appropriate and reliable data for parameter identification. Based on this, preload and temperature are taken into consideration in an ordinary KVBC model as two important additional factors. As a result, it leads to developing a novel model with new parameter identification, which is validated under different conditions. This new modeling method of rubber bush has three advantages. First, it shows improved accuracy for solving non-linear problems in a multi-body calculation, which is useful for researchers and vehicle engineers.

On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN.