基于遗传算法的车用轴向磁通电机温度模型优化

Optimization of Temperature Model in Axial Flux Motor Based on Genetic Algorithm for EVs

分段电枢轴向磁通电机基于其较高的转矩密度和紧凑的轴向尺寸,近年来被广泛应用于电动汽车领域。然而,由于分段电枢绕组与冷却结构接触位置的材料构成复杂,各点的接触压力难以确定,该区域的热导率始终是此类电机温度预测的难点。针对非理想接触面的传热行为,本文提出了一种在三维热阻网格模型基础上建立加权模型的研究方法以微调未知热导率。首先,介绍原理样机的拓扑结构,建立分段电枢单扇区的热阻网格模型以及加权模型框架。然后,通过遗传算法训练加权模型中的未知热导率,并使用该模型替换了传统的电机单扇区热阻网格模型。最后,该方法在原理样机实验台架中得到验证。

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.