English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate knowledge of temperatures in power semiconductor modules is crucialfor proper thermal management of such devices. Precise prediction oftemperatures allows to operate the system at the physical limit of the deviceavoiding undesirable over-temperatures and thus improve reliability of themodule. Commonly used thermal models can be based on detailed expert knowledgeof the device's physical structure or on precise and complete temperaturedistribution measurements. The latter approach is more often used in theindustry. Recently, we have proposed a linear time invariant state-spacethermal model based on a compartment representation and its identificationprocedure that is based on the Expectation-Maximization algorithm fromincomplete temperature data. However, the model still requires to measuretemperatures of all active elements. In this contribution, we aim to relax theneed for all measurements. Therefore, we replace the previous dark gray-boxapproach with a structured compartment model. The structure of the model isdesigned by a mesh-based discretization of the physical layout of the module.All compartments are assumed to share parameters that are identified from thedata of the measured elements. Temperatures of the unmeasured elements arepredicted using the shared parameters. Identification of the parameters ispossible only with suitable regularization due to limited amount of the data.In particular, the model tightening is accomplished by sharing parameters amongcompartments and by constraining the process covariance matrix of the model inthis contribution. Applicability of the proposed identification procedure isdiscussed in terms of growing state-space and therefore speeding up of theidentification algorithm is suggested. Performance of the proposed approach istested and demonstrated on simulated data.

Expectation-Maximization Algorithm for Identification of Mesh-based Compartment Thermal Model of Power Modules