Identification of Thermal Model of Power Module Using Expectation-Maximization Algorithm
| Autor: | Vaclav Smidl, Martin Votava, Jakub Sevcik |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
020209 energy Reliability (computer networking) Internal model Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology Temperature measurement Thermal Expectation–maximization algorithm Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Calibration State space power semiconductor module system identification thermal network business.industry 020208 electrical & electronic engineering expectation-maximization algorithm Power (physics) Identification (information) Semiconductor state space model Power module business Algorithm |
| Zdroj: | IECON |
| DOI: | 10.1109/iecon.2019.8927553 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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