Reduction of coupled field models for the simulation of electrical machines and power electronic modules

In automotive applications, the thermal dissipation of power electronics modules in mechatronic products is constantly increasing, whereas these products are confined in increasingly reduced volumes. During their operation, the semiconductor components and their environment are then submitted to severe electro-thermo-mechanical stresses that could cause their damage and lead to the product failure. The reliability and lifetime prevision of such products depend on the temperature junction located at the chip of power components. Furthermore, in order to ensure the safe operation of embedded applications, it is essential to perform a real-time control of thermal parameters such as the junction temperatures and power dissipated on the power components, in addition to the electrical and mechanical parameters. The objective of this thesis is to develop an identification method aimed at producing reduced thermal models to estimate the thermal behaviour of power electronic modules. Designed in a non-intrusive framework, this method post-processes the input data and the results produced by the numerical simulation of a detailed of the system under study. In this thesis, a new identification method, called "Kernel Identification Method" is developed. It has been validated on an industrial application dealing with a thermally coupled solid / fluid problem mainly governed by forced convection. An exploratory study of nonlinear problems identification where the natural convection plays the dominant role is then proposed. To this end, two identification methods of nonparametric nature are proposed: (i) a method based on the extension of the Kernel Identification Method; and (ii) a second method based on the "unscented" variant of the Kalman filter.