Closer attention has been given to the acoustic noise performance of electric motors as electrified powertrains penetrate into the transportation system. Particularly, switched reluctance machines (SRMs) introduce a new challenge to the acoustic noise aspects given that the radial force harmonics can excite the natural frequencies of the main circumferential modes.
A practical understanding of the radial force density decomposition is crucial in identifying the primary source of acoustic noise at different operating points, and it is one of the contributions of this thesis. An analytical expression is introduced to identify the temporal harmonic orders that excite different spatial mode shapes. The mode excitation is investigated along with the sound pressure level (SPL) produced by the primary vibrating mode shapes. Acoustic noise characteristics for each mode and the corresponding natural frequency at different speeds have been analyzed by using a waterfall plot.
The acoustic noise generation by conventionally controlled SRMs prevents its use on applications where acoustic comfort is required. Acoustic noise is radiated by the stator frame when a vibration mode is excited by the respective spatial order at a forcing frequency that is close to the stator's modal natural frequency. The excitation surface wave is the radial force density waveform as a function of time and spatial position. From the harmonic content analysis, a phase radial force shaping method is for switched reluctance machines is proposed.
A generic function for the radial force shape is identified, whose parameters are calculated by an optimization algorithm to minimize the torque ripple for a given average torque. From the phase radial force, a current reference is obtained. The proposed methodology is experimentally validated, with a four-phase 8/6 SRM, by acoustic noise measurements at different speeds and load torque conditions. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23996 |
| Date | 29 November 2018 |
| Creators | Dorneles Callegaro, Alan |
| Contributors | Emadi, Ali, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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