Thermal Digital Twin of Power Electronics Modules for Online Thermal Parameter Identification

The assessment of the state-of-health of power semiconductors and the use of thermal observers rely on precise knowledge of the thermal impedance of the device, which is hard to monitor online with state-of-the-art approaches. This work proposes thermal digital twins, which create a real-time capable digital replica of the physical thermal behavior and enable monitoring the thermal impedance online. The particle swarm optimization (PSO) algorithm and the dual extended Kalman filter (DEKF) are used to extract the thermal model for online monitoring. This is demonstrated for both approaches via a real-time simulation where the reference chip temperature is given by a digital thermal model. A comparison of the approaches is given and the DEKF-based approach is chosen for the implementation of a multi-chip model with thermal cross-coupling. The convergence of the DEKF-based digital twins is experimentally validated in the laboratory.