轮毂电机多目标优化设计与温升估计

doi:10.12382/bgxb.2024.0355

摘要/Abstract

摘要：

针对特种车辆电驱动系统高扭矩密度需求,为能够充分提升峰值扭矩能力和效率,降低扭矩波动,防止电机过热,提出了一种基于多物理场模型的轮毂电机多目标优化设计和温升估计方法。基于整车工况需求建立了轮毂电机电磁有限元模型和损耗模型。采用非支配排序遗传算法Ⅱ实现轮毂电机峰值扭矩、扭矩波动、效率、绕组换热面积的多目标优化,得到优化后电机电磁关键结构参数与损耗特性。基于几何结构建立了包含轮毂电机在内的电动轮热网络温升估计模型,预测了轮毂电机典型工况温升及温度分布特性。通过温升台架实验对温升预测模型精度进行了验证。研究结果表明,优化后轮毂电机峰值扭矩提升5.2%,峰值扭矩效率提升1.15%,端部绕组预测温度与实验结果对比均方根误差不超过4.3℃,计算速度大幅提升。

关键词: 轮毂电机, 多目标优化, 热网络模型, 温升估计

Abstract:

According to the high torque density requirement for electric drive system of special vehicle,a multi-physics-based multi-objective optimization design and temperature rise estimation method for in-wheel electric machine is proposed to effectively enhance the peak torque and efficiency,reduce the torque ripple and prevent the in-wheel electric machine from overheating.The electromagnetism finite element model and loss models of in-wheel electric machine are established based on the vehicle mission profile.Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ) is applied to optimize the peak torque,torque ripple,efficiency,and heat exchange area of winding.Based on the key geometry parameters and losses characteristics obtained,a temperature rise estimation model for electric wheel lumped parameter thermal network including the electric machine is established to estimate the temperature rise and distribution characteristics under typical working conditions.The accuracy of the temperature rise estimation model is validated through a testbench.The result shows that the peak torque and its efficiency of optimized in-wheel electric machine are increased 5.2% and 1.15%,respectively.The root mean square error of the estimated temperature is less than 4.3℃ compared with experimental result,and the calculation effort is dramatically reduced.

Key words: in-wheel electric machine, multi-objective optimization, thermal network model, temperature rise estimation

高琢, 李军求, 周阳, 张小鹏, 谭平, 邱锰, 朱家豪. 轮毂电机多目标优化设计与温升估计[J]. 兵工学报, 2025, 46(4): 240355-.

GAO Zhuo, LI Junqiu, ZHOU Yang, ZHANG Xiaopeng, TAN Ping, QIU Meng, ZHU Jiahao. Multi-objective Optimization Design and Temperature Rise Estimation of In-wheel Electric Machine[J]. Acta Armamentarii, 2025, 46(4): 240355-.

图/表 22

图1 内转子电动轮结构

Fig.1 Inner rotor E-wheel structure

图2 轮毂电机优化设计过程

Fig.2 Design and optimization process of in-wheel electric machine

图3 硅钢片铁损特性

Fig.3 Loss characteristics of silicon steel plate

图4 电机热网络示意图

Fig.4 Schematic diagram of electric machine thermal network

表1 电机热网络节点换热计算方法

Table 1 Heat exchange calculation method of electric machine thermal network nodes

热阻计算区域	传热方向	等效热阻计算方法
r1、r2、r4、s5	径向	$\frac{1}{2 \pi \lambda L_{c}} \ln \left(\frac{r_{o}}{r_{i}}\right)$
r3、r5、s2	径向	$\frac{1}{\varphi \lambda L_{c}} \ln \left(\frac{r_{o}}{r_{i}}\right)$
r4-s2	径向	$\frac{L_{c}}{N u \lambda A}$
s1、s3、s3、s4	径向	$\begin{array}{l}\frac{L_{C}}{\lambda\left(A_{o}-A_{i}\right)} \ln \left(\frac{x_{o}}{x_{i}}\right) \text {（梯形）；}\\\frac{L_{c}}{\lambda A}(\text { 矩形 })\end{array}$
s1、s4	周向	$\frac{\varphi}{2 \lambda l} \cdot \frac{r_{i}+r_{o}}{r_{o}-r_{i}}$

图5 电动轮热网络

Fig.5 E-wheel thermal network

图6 电机结构及关键优化设计参数

Fig.6 Structure and key design parameters of electric machine

图7 NSGA-Ⅱ优化流程示意

Fig.7 NSGA-Ⅱ optimization procedure

图8 优化结果分布

Fig.8 Optimized results

表2 优化结果对比

Table 2 Comparison of objectives before and after optimization

优化目标	初始设计	优化设计	优化结果对比/%
$T ¯$ /(N·m)	1147.41	1207.62	5.2
$Δ T T ¯$ /%	3.59	3.65	0.06
η_em/%	85.58	86.73	1.15
A_s/mm²	3419.8	5142.16	50.4

表2 优化结果对比

Table 2 Comparison of objectives before and after optimization

优化目标	初始设计	优化设计	优化结果对比/%
$T ¯$ /(N·m)	1147.41	1207.62	5.2
$Δ T T ¯$ /%	3.59	3.65	0.06
η_em/%	85.58	86.73	1.15
A_s/mm²	3419.8	5142.16	50.4

图9 电机磁密云图

Fig.9 Magnetic density of electric machine

图10 两种典型工况下优化前后扭矩输出对比

Fig.10 Torque outputs before and after optimization under 2 typical working conditions

图11 两种典型工况下扭矩谐波成分

Fig.11 Torque harmonics under 2 typical working conditions

表3 两种典型工况的扭矩波动

Table 3 Torque ripples under 2 working conditions

工况	波动幅值/(N·m)	幅值占比/%	THD/%
峰值扭矩/功率	43.9	3.6	2.9
最高转速	8.9	7.8	6.0

图12 电机各部件损耗特性

Fig.12 EM loss map

图13 电机各部件在2种典型工况下的损耗

Fig.13 Loss of EM components under 2 typical working conditions

图14 峰值扭矩/功率工况电机各部件温升

Fig.14 Temperature rise of EM components under the working conditions of peak torque/power

图15 最高转速工况电机各部件温升

Fig.15 Temperature rise of EM components at maximum speed

图16 实验台架及温度传感器安装位置

Fig.16 Installation position of testbench and temperature sensor

图17 峰值扭矩/功率工况的端部绕组温升验证

Fig.17 Validation of the end windings temperature rise estimation under the working conditions of peak torque/power

图18 最高转速工况的端部绕组温升验证

Fig.18 Validation of the end windings temperature rise estimation at maximum speed

图19 高温散热工况的端部绕组温升验证

Fig.19 Validation of the end windings temperature rise estimation at high environment temperature

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