Heat Pipe Performance Characteristics and Prediction Models for Motor Heat Dissipation

Li Xueqing; Zhang Zhiqing; Zhang Yifan; Lin Xinyue; Liu Shengchun; Zhang Chengming

doi:10.12465/issn.0253-4339.20260601001

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Heat Pipe Performance Characteristics and Prediction Models for Motor Heat Dissipation

更新时间：2026-08-17

- Heat Pipe Performance Characteristics and Prediction Models for Motor Heat Dissipation
- Journal of Refrigeration Pages: 1-8(2026)
- 作者机构：
  
  1.天津商业大学天津市制冷技术重点实验室，天津 300134
  2. 哈尔滨工业大学电气工程及自动化学院，哈尔滨 150006
- 作者简介：
  
  Liu Shengchun， male， professor， School of Mechanical Engineering， Tianjin University of Commerce， 86-13920682426， E-mail： liushch@tjcu.edu.cn. Research fields： trans critical CO2 heat pump systems， system optimization and energy efficiency enhancement， refrigeration and freezing technologies， phase change materials for thermal energy storage and renewable energy utilization.
- 基金信息：
  
  the National Natural Science Foundation of China(52077044)
- DOI：10.12465/issn.0253-4339.20260601001
  CLC： TK172.4;TM301.4;TP181
- Received：01 June 2026，
  
  Revised：2026-06-22，
  
  Accepted：25 June 2026，
  
  Online First：17 August 2026，
- 稿件说明：
移动端阅览
李雪强，张植青，张一凡，等. 应用于电机散热的热管性能特性及预测模型对比［J］. 制冷学报，XXXX，XX（XX）：1-8.

Li Xueqing，Zhang Zhiqing，Zhang Yifan，et al. Heat Pipe Performance Characteristics and Prediction Models for Motor Heat Dissipation［J］. Journal of Refrigeration，XXXX，XX（XX）：1-8.
李雪强，张植青，张一凡，等. 应用于电机散热的热管性能特性及预测模型对比［J］. 制冷学报，XXXX，XX（XX）：1-8. DOI： 10.12465/issn.0253-4339.20260601001.

Li Xueqing，Zhang Zhiqing，Zhang Yifan，et al. Heat Pipe Performance Characteristics and Prediction Models for Motor Heat Dissipation［J］. Journal of Refrigeration，XXXX，XX（XX）：1-8. DOI： 10.12465/issn.0253-4339.20260601001.

摘要

电机高功率密度化使其热管理问题日趋严峻，准确预测热管等效导热系数对电机散热设计至关重要。针对电机散热系统中热管性能及性能预测问题，本文以等效导热系数为评价指标，实验研究了热源功率、冷凝段与蒸发段的相对位置、弯折角度等关键参数对热管传热性能的影响机制。在此基础上建立并对比了遗传算法优化的反向传播神经网络（GA-BPNN）、卷积神经网络（CNN）、最小二乘支持向量机（LSSVM）、随机森林（RF）与极端梯度提升（XGBoost）5种机器学习模型的性能。结果表明：重力与弯折角度对热管的导热性能影响最大，上方侧90°弯折的热管导热性能最好，其导热性能是下方侧的30倍；XGBoost模型的平均绝对百分比误差为3.72%，决定系数为0.95，均方根误差仅次于GA-BPNN模型，在所选的5种模型中的精度最高，且训练时间小于2 s；XGBoost模型的预测表明不同安装位置及弯折角度的热管性能不尽相同，为了提高电机各绕组的均温性，推荐弯折角度为0°的热管用于电机散热中。

Abstract

Electric motors are developing towards high power density and miniaturization， making thermal management increasingly critical. This study investigated the influence mechanisms of key parameters， such as the heat source power， position between evaporator and condenser side， and bending angle， on the heat transfer performance of heat pipe. Moreover， comparative analyses were performed on the prediction performance of five machine learning models： genetic algorithm optimized back propagation neural network （GA-BPNN）， convolutional neural network （CNN）， least squares support vector machine （LSSVM）， random forest （RF）， and extreme gradient boosting （XGBoost）. The results indicate that： the coupled detrimental effects of gravity and bending angle on the capillary wick structure govern the heat transfer performance； under a 90° bending angle at 60-W heat source power， the side arrangement of the condenser above evaporator achieves the best equivalent thermal conductivity， which is 30 times that of the condenser under evaporator. The mean absolute percentage error of XGBoost is 3.72%， coefficient of determination is 0.95， root mean square error only higher than that of the GA-BPNN model， and training time is less than 2 s， demonstrating the best performance among five models. The prediction results of XGBoost model illustrate that different positions and bending angles lead to different dissipation performances. To improve the temperature uniformity in the winding of motor， a heat pipe with 0° bending angle is recommended for the heat dissipation of motor.

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