电子测量与仪器学报

赵泽宇,杜明星.多数据驱动人工神经网络的 IGBT 结温在线估计方法[J].电子测量与仪器学报,2022,36(7):223-229

多数据驱动人工神经网络的 IGBT 结温在线估计方法

On line estimation of IGBT junction temperature based onmulti data driven artificial neural network

DOI：

英文关键词:IGBT junction temperature estimation package degradation data driven neural network

基金项目:天津市技术创新引导专项（20YDTPJC00510）项目资助

作者	单位
赵泽宇	1.天津理工大学天津市复杂系统控制理论及应用重点实验室
杜明星	1.天津理工大学天津市复杂系统控制理论及应用重点实验室

Author	Institution
Zhao Zeyu	1.Tianjin Key Laboratory of Control Theory & Applications in Complicated System, Tianjin University of Technology
Du Mingxing	1.Tianjin Key Laboratory of Control Theory & Applications in Complicated System, Tianjin University of Technology

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中文摘要:

传统结温估计方法因其无法根据 IGBT 模块健康状态实时调校,从而导致当模块发生封装退化后无法准确估计结温。因此,为解决在实际工况中模块封装退化造成的结温估计误差问题,建立了一个基于多数据驱动的以人工神经网络为主体的 IGBT 结温在线估计模型。首先,确定饱和压降作为温敏电参数并研究其构成,分析其与集电极电流,芯片结温和封装退化之间的耦合关系。随后,为解决封装退化造成的饱和压降温度特性变化问题,提出结合米勒电压温度特性的优势,配合饱和压降与集电极电流驱动人工神经网络算法构建结温估计模型,并通过搭建实验平台提取数据,完成模型的训练。最终,通过与传统结温估计方法对比估计误差,新模型将结温估计误差从 20%降低到了 5%以下。

英文摘要:

Traditional junction temperature estimation methods cannot be adjusted according to the health status of IGBT module in real time, which leads to inaccurate junction temperature estimation when the module is degraded. Therefore, to solve the problem of junction temperature estimation error caused by module package degradation in actual conditions, this paper established a multi-data-driven IGBT junction temperature online estimation model with artificial neural network as main body. Firstly, the saturation voltage drop was determined as a thermoelectric parameter and its composition was studied. The coupling relationship between the saturation voltage drop, collector current, chip junction temperature and package degradation are analyzed. Then, to solve the problem of temperature characteristic change of saturation voltage drop caused by package degradation, a junction temperature estimation model was constructed by combining the advantages of Miller voltage temperature characteristic and the artificial neural network algorithm driven by saturation voltage drop and collector current. And the data were extracted by building an experimental platform to complete the training of the model. Finally, by comparing the estimation error with the traditional junction temperature estimation method, the new model reduces the estimation error from 20% to about 5%.

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