Machine Learning Approach to Model Junction Temperatures in Automotive Inverters
This paper presents a machine-learning approach to model semiconductor junction temperatures.
Increasing power density of automotive inverters lead to an increasing demand for accurate lifetime and reliability models. As such models are closely dependent on junction temperatures, they benefit from accurate temperature estimation methods. The model presented in this paper was trained and evaluated with data from a test bench incorporating a 1200 V SiC power module. The data pipeline, model performance, benefits and limitations are shown and discussed.
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