Device-Sensor Assembly FEA Modeling to Support Kalman-Filter-Based Junction Temperature Monitoring
| Autor: | Alessandro Soldati, Carlo Concari, Nicola Delmonte, Paolo Cova |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Computer science 020208 electrical & electronic engineering Thermistor Energy Engineering and Power Technology Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology Kalman filter Converters 01 natural sciences 0103 physical sciences Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Electronic engineering Power semiconductor device Junction temperature Electrical and Electronic Engineering Power MOSFET Datasheet Voltage |
| Zdroj: | IEEE Journal of Emerging and Selected Topics in Power Electronics. 7:1736-1747 |
| ISSN: | 2168-6785 2168-6777 |
| DOI: | 10.1109/jestpe.2019.2922939 |
| Popis: | Junction temperature monitoring for power devices is an essential requirement for high-reliability applications. Temperature sensitive electrical parameters (TSEPs) are powerful tools in this process, but to achieve sufficient accuracy they require complex characterization procedures for each part, that can hardly be implemented in mass-produced converters. This paper proposes a combination of interdisciplinary approaches to ultimately solve this problem for power MOSFETs by an automated procedure that can occur in-place, with devices mounted and without any specific user intervention nor additional heating components. The TSEP exploited in this paper is the ON-state drain–source voltage of the power MOSFET. A wide-bandwidth ON-state voltage sensing circuit and a low-cost thermistor conditioning circuit to sense the case temperature of the device are presented and modeled. A lumped parameters thermal model of the system is given, and finite-element method (FEM) simulations are employed to obtain first-guess values for the unknown thermal network parameters, integrating information from the device datasheet. Finally, an observer based on the Kalman filter applied to the data collected from these sources is presented and evaluated experimentally. Performance is assessed with the use of thermal imaging techniques. |
| Databáze: | OpenAIRE |
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