Optimal design of a coreless stator axial flux permanent-magnet motor based on particle swarm algorithm

The particle swarm optimization (PSO) algorithm and the improved algorithm are used to optimize an three-phase axial flux permanent-magnet (AFPM) motor with 8 pair poles. The objective is to maximize the motor power density, while ensuring the output power, acceptable current density, desired peak value of magnetic flux density and efficiency. Five variables are selected which are the PM thickness, air-gap clearance, pole width to pole pitch ratio, stator winding thickness and inner radius. Firstly, the performance of the AFPM motor is calculated and the model of optimization is built. Then three-dimensional finite-element analysis (FEA) is carried out to simulate the boundaries of the variables. Finally, the AFPM motor is optimized based on the PSO algorithm. The results indicate that the motor power density increases obviously with all the constraint conditions satisfied. PSO algorithm combined with EFA shows great advantage in the optimal of motor.