Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

Jingwei Zhang, Honghua Wang, Sa Zhu and Tianhang Lu
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Jingwei Zhang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Honghua Wang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Sa Zhu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Tianhang Lu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2019, vol. 12, issue 12, 1-18

Abstract: The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced.

Keywords: bearingless switched reluctance motor (BSRM); lumped-parameter thermal model; multi-physics approach; multi-objective optimization; optimal design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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