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

• URL: http://arxiv.org/abs/2504.00133v1
• Date: Mon, 31 Mar 2025 18:37:14 GMT
• Title: Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation
• Authors: Mattia Scarpa, Francesco Pase, Ruggero Carli, Mattia Bruschetta, Franscesco Toso,
• Abstract summary: Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications.<n>This paper presents a novel hybrid framework that combines physics-based thermal modeling with data-driven techniques to identify and correct power losses accurately using only temperature measurements.
• Score: 1.9997803560872798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 physics-based 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 reduced-order 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{\deg}C to 0.3+-0.3{\deg}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. This methodology allows us to accurately estimate power losses without direct measurements, making it particularly helpful for real-time industrial applications where sensor placement is hindered by cost and physical limitations.

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