Optimization Design Research of Dual-Airgap High-Torque Permanent Magnet Motor Based on Coevolution Algorithm

To achieve the high-quality operation of the ship, this paper takes the dual-airgap high-torque permanent magnet motor as the research object and proposes an optimization method based on the co-evolutionary algorithm. Aiming at the multivariable, strongly coupled, and nonlinear characteristics of this type of motor, the paper first carried out sensitivity analysis on the structural parameters of the motor, and then established a surrogate model with the motor output torque and torque ripple as the optimization objectives through BP neural network, and finally carried out multi-objective optimization of the motor based on the co-evolutionary algorithm. Finite element analysis verifies that the optimized design significantly improves the motor's torque density and greatly reduces torque ripple, offering a new approach and methodology for the optimal design of dual-air-gap motors.