论文标题
预期最大化算法用于鉴定基于网格的电源模块的网隔室热模型
Expectation-Maximization Algorithm for Identification of Mesh-based Compartment Thermal Model of Power Modules
论文作者
论文摘要
对电源半导体模块中温度的准确了解对于适当的热管理此类设备至关重要。温度的精确预测允许在设备的物理限制下操作系统，从而避免了不良的过度植物，从而提高了模块的可靠性。常用的热模型可以基于对设备物理结构的详细专家知识，也可以基于精确和完整的温度分布测量。后一种方法在行业中经常使用。最近，我们提出了一个基于隔室表示及其识别过程的线性时间不变的状态空间模型，该模型基于不完整温度数据的期望最大化算法。但是，该模型仍需要测量所有活性元素的温度。在这项贡献中，我们旨在放松对所有测量的需求。因此，我们用结构化隔室模型替换了先前的深灰盒方法。模型的结构是通过基于网格的模块物理布局的离散化设计的。假定所有隔间都共享从测量元素数据中识别的参数。使用共享参数预测未衡量元素的温度。由于数据量有限，因此只有在适当的正则化的情况下才能识别参数。特别是，通过在隔室之间共享参数并在此贡献中约束模型的过程协方差矩阵来完成模型的收紧。提出的识别程序的适用性是根据状态空间的增长讨论的，因此建议加快识别算法。在模拟数据上测试并证明了所提出的方法的性能。
Accurate knowledge of temperatures in power semiconductor modules is crucial for proper thermal management of such devices. Precise prediction of temperatures allows to operate the system at the physical limit of the device avoiding undesirable over-temperatures and thus improve reliability of the module. Commonly used thermal models can be based on detailed expert knowledge of the device's physical structure or on precise and complete temperature distribution measurements. The latter approach is more often used in the industry. Recently, we have proposed a linear time invariant state-space thermal model based on a compartment representation and its identification procedure that is based on the Expectation-Maximization algorithm from incomplete temperature data. However, the model still requires to measure temperatures of all active elements. In this contribution, we aim to relax the need for all measurements. Therefore, we replace the previous dark gray-box approach with a structured compartment model. The structure of the model is designed by a mesh-based discretization of the physical layout of the module. All compartments are assumed to share parameters that are identified from the data of the measured elements. Temperatures of the unmeasured elements are predicted using the shared parameters. Identification of the parameters is possible only with suitable regularization due to limited amount of the data. In particular, the model tightening is accomplished by sharing parameters among compartments and by constraining the process covariance matrix of the model in this contribution. Applicability of the proposed identification procedure is discussed in terms of growing state-space and therefore speeding up of the identification algorithm is suggested. Performance of the proposed approach is tested and demonstrated on simulated data.
