Application of phononic crystals for vibration reduction and noise reduction of wheel-driven electric buses based on neural networks

Abstract

Because of the position of the motor and the excitation of the suspension system, a wheel-driven electric bus produces low-frequency noise, which is difficult to resolve through traditional sound absorption and noise reduction technology. Through an interior noise test of a wheel-driven electric bus, we found that the interior low-frequency noise had a considerable influence on the driver. In order to solve this problem, a locally resonant phononic crystal was used to meet the requirements of vibration and noise reduction for the wheel-driven electric bus. The intrinsic relationship between the band gap distribution of the locally resonant phononic crystal and the topology was established by training a neural network, so as to achieve the desired effect of the bandgap model on the basis of the input bandgap range. Upon an increase in the number of models, the prediction model error decreased gradually. This method could quickly obtain the structural parameters of the locally resonant phononic crystal with the expected band gap, which made it convenient to apply locally resonant phononic crystals to the vibration and noise reduction of wheel-driven electric buses and in other fields.