期刊
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 58, 期 3, 页码 3533-3544出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2022.3159326
关键词
Force; Vibrations; Electromagnetics; Electric machines; Sensitivity analysis; Optimization; Stator windings; Electric machine; noise; vibration; and harshness (NVH); optimization; permanent magnet synchronous machine (PMSM); sensitivity analysis
This article proposes a sensitivity analysis method based on the response of structural units, and studies the impact of ten design parameters in SPMSM in detail. A fast optimization strategy based on a multilevel nonlinear regression model is introduced. Experimental results validate the accuracy of simulation predictions.
Acoustic noise and vibration sources in electric machines are mainly of electromagnetic and structural origin. A lumped structural unit response-based sensitivity analysis procedure is proposed in this article, which isolates electromagnetic and structural impacts brought by variation of different design parameters in a fractional slot concentrated winding, surface mounted permanent magnet synchronous machine (SPMSM). The impact of ten design parameters on average torque, torque ripple, synchronous inductance, dominant spatial-temporal order airgap force, cogging torque, friction torque, dominant mode frequency and structural unit response is studied in detail for a 12 slot/10 pole (12s10p) SPMSM. Analysis reveals that on a 12s10p SPMSM, slot opening has a very high impact on dominant airgap force component. A multilevel nonlinear regression model-based fast optimization strategy is introduced considering electromagnetic and structural design objectives and constraints following the sensitivity analysis. The optimized design is prototyped to validate the proposed design approach. Impact hammer-based modal analysis, no load, load and run-up tests are performed on the prototype for experimental validation. Ranging from structural tests to electromagnetic tests, the experimental results closely follow and validate the simulation-based predictions.
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