Genetic and random search algorithms for optimising vehicle interior noise and vibration

Abstract

The goal of this paper is to optimise the vehicle noise and vibration using genetic and random search algorithms (RSAs) approach. Toward this end, an experimental design was carried out to acquire the noise and vibration data using three local compact cars namely Viva, Myvi and Axia on stationary and running conditions with a variety of engine speeds transmissions. The measured noise and vibration were analysed to obtain the sound quality parameters loudness and sharpness, sound pressure level and vibration exposures in the interior vehicle cabin. Overall findings of this study indicate that the comfort level is factually influenced by factors including type of road surface, engine transmissions and vehicle design characteristics. The results also indicate that the proposed approach is reliable and can be utilised by automotive researchers to identify the optimal NVH values necessary for vehicle refinement and noise control.