Optimization Design of Dual-Parallel Rotor Permanent Magnet Motor Based on Dynamic Kriging Surrogate Model

In order to reduce the torque ripple and the unbalanced electromagnetic force on the rotor of the dual-parallel rotor permanent magnet motor, a dynamic Kriging surrogate model is proposed to optimize its structural parameters. In the process of constructing the dynamic Kriging surrogate model, the concept of key sampling space is introduced, which solves the problems of low optimization efficiency and poor model accuracy of the traditional static surrogate model based on 'one-time' sampling. The topological structure of the dual-parallel rotor permanent magnet motor is introduced, and a prototype is used to verify the accuracy of the numerical model. The optimization parameters are determined, and the initial sampling space of each optimization parameter is determined according to the influence law of a single parameter on the optimization objectives. The initial sample database of the Kriging surrogate model is established, and a dynamic criterion for adding sample points is proposed. Combined with the NSGA-II algorithm, the surrogate model is constructed and solved. The optimal solution is substituted into the numerical model, which verifies the feasibility and correctness of the proposed optimization design method. The accuracy of the dynamic Kriging surrogate model is discussed and compared with the traditional static surrogate model. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.