<ul><li>A novel hybrid framework combining physics-based thermal modeling with data-driven techniques has been developed for accurate power loss identification in power electronics.</li><li>The framework leverages a cascaded architecture with a neural network that corrects the outputs of a nominal power loss model using temperature measurements.</li><li>Two neural architectures, a bootstrapped feedforward network and a recurrent neural network, were explored, with the feedforward approach achieving superior performance and computational efficiency.</li><li>Experimental results demonstrate that the hybrid model reduces temperature estimation errors and power loss prediction errors compared to traditional physics-based approaches, even in the presence of uncertainties.</li></ul>

Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation

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