Gurav, R., Udawant, K., Rajamanickam, R., Karanth, N. et al., "Mechanical and Aerodynamic Noise Prediction for Electric Vehicle Traction Motor and Its Validation," SAE Technical Paper 2017-26-0270, 2017, doi:10.4271/2017-26-0270.
With emission norms getting more and more stringent, the trend is shifting towards electric and hybrid vehicles. Electric motor replaces engine as the prime mover in these vehicles. Though these vehicles are quieter compared to their engine counterpart, they exhibit certain annoying sound quality perception. There is no standard methodology to predict the noise levels of these motors. Electric motor noise comprises of mainly three sources viz., Aerodynamic, Electromagnetic and Mechanical. A methodology has been developed to predict two major noise sources of electric motor out of the three above viz. Mechanical and Aerodynamic noise. These two noise sources are responsible for the tonal noise in an electric motor. Aerodynamic noise arises most often around the fan, or in the vicinity of the machine that behaves like a fan. This noise is predominant at higher motor speed and also in electric vehicle due to higher speed fluctuation. Prediction of the aerodynamic noise primarily has been done using Computational Fluid Dynamic tool. Ffowcs Williams-Hawkings (FW-H) model is used to predict the aerodynamic noise. The Mechanical noise is predominant at the intermediate speed of the motor. The vibrations caused by this noise source results in unsatisfactory driving experience for the end user. Free-free modal analysis is performed for calculating the mode shapes and modal frequencies of the motor. Further frequency response analysis is carried out to calculate the surface vibration velocity of the motor. Sound power evaluation of the motor has been carried out using a Boundary Element Method (BEM) tool. The aerodynamic noise was validated with experimental measurement and it was found to be in good agreement. This methodology has helped in evaluating the motor performance at prototype stage itself hence reducing product development cycle time.