Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects

Abstract

This letter proposes an artificial intelligence-aided thermal model for power electronic devices/systems considering thermal cross-coupling effects. Since multiple heat sources can be applied simultaneously in the thermal system, the proposed method is able to characterize model parameters more conveniently compared to existing methods where only single heat source is allowed at a time. By employing simultaneous cooling curves, linear-to-logarithmic data re-sampling, and differentiated power losses, the proposed artificial neural network-based thermal model can be trained with better data richness and diversity while using fewer measurements. Finally, experimental verifications are conducted to validate the model capabilities.