Torque Optimization of Building-Block Transverse Flux Permanent Magnet Motor Based on QGAs

Abstract

Building-block transverse flux permanent magnet motor(B-TFPMM) has the characteristics of low-speed high-torque and small axial size, which is suitable as the hub motor of direct-driving electric vehicle. However, the torque ripple of B-TFPMM is large, which would affect the driving safety and riding comfort. To optimize the motor torque, the topological structure and working principle of B-TFPMM are analyzed firstly, and the analytical model of B-TFPMM torque ripple rate is derived. Then, two quantum genetic algorithms (QGAs) with different evolution processes are presented, an optimal individual selection scheme considering the torque ripple and the average torque simultaneously are proposed and the main motor sizes that affecting the torque ripple are optimized. The result shows that one of the strategies has more obvious suppression effect on torque ripple, while the other one has more obvious optimization effect on average torque. Therefore, the appropriate strategy could be selected according to the specific need.Keywords: Quantum genetic algorithm • Building-block transverse flux permanent magnet motor • Torque optimization • Motor design