Recently, flux modulated permanent magnet motors have aroused considerable attention, due to the merits of relatively high output torque at low speed. To further improve motor torque output capability, double stator vernier permanent magnet (DS-VPM) motors have attracted increasing attention 1. However, due to the relatively complicated relationship among airgap flux harmonic, motor structure and performances, the difficulty and time burden of the optimization design of DSVPM motor is increased significantly 2. Besides, during actual motor manufacturing process, there exists many unavoidable design uncertainties, which would result in motor performance degradation 3. Thus, it has been a hot but challenging issue on how to realize high-efficiency optimization design of DS-VPM motor consider parameter fluctuation. In this paper, an airgap-harmonic-based multi-level robust optimization design method is proposed for DS-VPM motor. By the proposed optimization method, motor performances can be improved comprehensively considering parameter fluctuation. The structure of the investigated DS-VPM motor is shown in Fig. 1 (a), and corresponding design parameters of DS-VPM motor are presented in Fig. 1 (b). The flowchart of the proposed airgap-harmonic-based multi-level robust optimization method is presented in Fig. 2. Firstly, the detailed relationship among design parameters, design parameters, and motor performances are investigated, as shown in Fig. 3 and Fig. 4. Then, based on multi-objective genetic algorithm and response surface method, deterministic optimal design of DS-VPM motor is determined. Furthermore, robust optimization method is utilized to achieve the robust optimal motor deign. Electromagnetic performances of the optimal DS-VPM motor are presented in Fig. 5 and Fig. 6, while the normal distribution of the initial and final motor performances is presented in Fig. 7. Consequently, it is noted that that not only motor performances are improved effectively, but the fluctuations of the motor performances are reduced significantly which verifies the effectiveness of the proposed robust multi-level optimization method. More simulation and experimental results will be presented in the full paper.

References:

1 W. Zhao, T. A. Lipo and B. -I. Kwon, IEEE Trans. Magn, vol. 51, no. 11, pp. 1-4, Nov 2015.
2 X. Zhu, M. Jiang and Z. Xiang, IEEE Trans. Ind. Electron, vol. 67, no. 7, pp. 5337-5348, July 2020.
3 M.-M. Koo, J.-Y. Choi and K. Hong, IEEE Trans. Magn, vol. 51, no.11, pp. 1-4, Nov 2015.