Identification of thermal model of power module using expectation-maximization algorithm

Date issued

2019

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Abstract

Prediction of junction temperatures in power semiconductor modules is essential to improve reliability of the device and prevent module failures due to thermal stress. Lumped parameter network is a popular approach for temperature modeling. Calibration of the thermal model is based on thermal measurements of the junction temperatures that are difficult to obtain. We aim to combine the knowledge of internal model structure and as little measurements as possible. Specifically, we use a state space thermal model with structure determined by the module layout, and propose to use the Expectation-Maximization algorithm from that can utilize data from different incomplete experiments. The identification procedure is introduced in detail in this paper and the applicability of the proposed approach is demonstrated on simulated and experimental data.

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Citation

ŠEVČÍK, J., ŠMÍDL, V., VOTAVA, M. Identification of thermal model of power module using expectation-maximization algorithm. In: Proceedings : IECON 2019 : 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway: IEEE, 2019. s. 1-7. ISBN 978-1-72814-878-6.
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