基于遗传算法的车用轴向磁通电机温度模型优化

In recent years，segmented armature axial flux motors have been widely used in the field of electric vehicles with the high torque density and compact axial size. However，due to the complex material composition of the contact area between segmented armatures and cooling fins，and the difficulties in determining the pressure at each position，the thermal conductivity of this region is always the difficulty of temperature prediction for such motors. For the heat transfer behavior of non-ideal contact surface，a research method of building a weighted model based on the three-dimensional thermal resistance grid model is proposed in this paper to fine-tune the unknown thermal conductivity. Firstly，the topology of the prototype is introduced，and the thermal resistance grid model and the weighted model framework of the segmented armature single sector are established. The unknown thermal conductivity in the weighted model is trained by genetic algorithm，and the model is used to replace the traditional single-sector thermal resistance grid model of the motor. Finally，the method is verified by the experimental bench of the prototype motor.