Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 68, Issue 1, Pages 139-148Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.2965463
Keywords
Cross-factor variance analysis; finite element analysis; interior permanent magnet synchronous motor (IPMSM); multilevel optimization; multiobjective optimization; Pearson correlation coefficient analysis
Categories
Funding
- National Natural Science Foundation of China [51875261]
- Natural Science Foundation of Jiangsu Province of China [BK20180046, BK20170071]
- Qinglan Project of Jiangsu Province
- Key Project of Natural Science Foundation of Jiangsu Higher Education Institutions [17KJA460005]
- Six Categories Talent Peak of Jiangsu Province [2015-XNYQC-003]
- Postgraduate Research & the Practice Innovation Program of Jiangsu Province [KYCX17_1815]
- State Scholarship Fund of China Scholarship Council [201908320298]
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This article introduces a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. By using Pearson correlation coefficient analysis and cross-factor variance analysis, a three-level optimization structure is obtained based on the correlations of design parameters and optimization objectives. The use of Kriging model to approximate finite element analysis improves optimization efficiency and provides better design solutions.
The multiobjective optimization design of interior permanent magnet synchronous motors (IPMSMs) is a challenge due to the high dimension and huge computation cost of finite element analysis. This article presents a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. To determine the multilevel optimization strategy, Pearson correlation coefficient analysis and cross-factor variance analysis techniques are employed to evaluate the correlations of design parameters and optimization objectives. A three-level optimization structure is obtained for the investigated IPMSM based on the analysis results, and different optimization parameters and objectives are assigned to different levels. To improve the optimization efficiency, the Kriging model is employed to approximate the finite element analysis for the multiobjective optimization in each level. It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly. Finally, experimental results based on a prototype are provided to validate the effectiveness of the proposed optimization method. The proposed method can be applied for the efficient multiobjective optimization of other electrical machines with high dimensions.
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