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

Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications. This paper presents a novel hybrid framework that combines physicsbased thermal modeling with data-driven techniques to identify and correct power losses accurately using only temperature measurements. Our approach leverages a cascaded architecture where a neural network learns to correct the outputs of a nominal power loss model by backpropagating through a reducedorder thermal model. We explore two neural architectures, a bootstrapped feedforward network, and a recurrent neural network, demonstrating that the bootstrapped feedforward approach achieves superior performance while maintaining computational efficiency for real-time applications. Between the interconnection, we included normalization strategies and physics-guided training loss functions to preserve stability and ensure physical consistency. Experimental results show that our hybrid model reduces both temperature estimation errors (from 7.2±6.8°C to 0.3±0.3°C) and power loss prediction errors (from 5.4±6.6W to 0.2±0.3W) compared to traditional physics-based approaches, even in the presence of thermal model uncertainties, making it particularly helpful for real-time industrial applications.