Electromagnetic Analysis and Optimization of Ferrite‐Assisted Double‐Layer Halbach Permanent–Magnet Vernier Motor Based on Improved Cuckoo Search Grey Wolf Optimization Algorithm

In order to improve the electromagnetic torque and stability of the drive motor for belt conveyors, and considering economic benefits, two ferrite assisted double‐layer nonuniform Halbach array consequent‐pole (DNHC‐permanent magnet synchronous motors[PMVM]) are proposed: DNHC‐PMVMA and DNHC‐PMVMB. The main magnetic pole of DNHC‐PMVM rotor adopts DNH rare earth permanent magnet, and ferrite is used as the auxiliary magnetic pole of stator and rotor. The rationality of the proposed structure is verified by comparing and analyzing PMVM, DNHC‐PMVMA, and DNHC‐PMVMB by finite method. In order to further optimize the motor structure, the cuckoo search (CS) grey wolf optimization (CSGWO)algorithm is improved, and the improvement strategies such as circle chaotic mapping are introduced. After multiobjective optimization test, it is proved that the comprehensive performance of improved CSGWO (ICSGWO) is better than that of CSGWO, GWO algorithm, particle swarm optimization (PSO), and other algorithms. Based on the response surface method (RSM), ICSGWO, and parameter scanning, the three motor structures are optimized, respectively. The finite element method is used to analyze the three optimized motors. The results show that the performance parameters such as electromagnetic torque and torque ripple are significantly improved, which verifies the effectiveness of the optimization method. Meanwhile, the performance parameters of DNHC‐PMVM are significantly better than those of PMVM, which proves the superiority of the proposed structure.