  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.
