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Multi-physics Modeling of Electromagnetically Excited Acoustic Noise of Induction Motor
Technical Paper
2021-01-0772
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
For electric vehicles, electromagnetically excited noise from the traction motor is one of the main acoustic noise sources, especially for automobiles driven at low speed that mechanical noise and aerodynamic noise are minor. To analyze the characteristics of the electromagnetically excited noise and propose noise reduction suggestions, an accurate noise prediction model is essential. In this paper, a multi-physics model to predict the electromagnetic force excited acoustic noise of induction motor is presented. First, a Three-Dimensional (3D) transient electromagnetic model of the motor was established using the Finite Element Method (FEM). By inputting the current signal collected in the noise test as the current source in the FEM model, the uneven distributed time-varying magnetic forces, which included the influence of the current harmonics due to Pulse-Width Modulation (PWM), was calculated. Then, a structural model was built. The anisotropic characteristic of silicon sheets stacked stator core, as well as the influence of windings, were considered by assigning orthotropic equivalent material parameters to the stator structural model. Through mesh mapping, the FEM calculated magnetic forces were transferred onto the structural model and the forced vibration was calculated using the modal superposition method. Base on the vibrational speed of the motor shell, the acoustic noise was calculated using the Boundary Element Method (BEM). Finally, an experiment to test the noise of the studied motor in a semi-anechoic room was introduced. As the simulated results showed good accordance with the measured data, the accuracy of the model was verified
Citation
Hu, X., Zuo, S., and Li, F., "Multi-physics Modeling of Electromagnetically Excited Acoustic Noise of Induction Motor," SAE Technical Paper 2021-01-0772, 2021, https://doi.org/10.4271/2021-01-0772.Data Sets - Support Documents
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