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The non-linear property of magnetics poses challenges for their loss modelling in power electronics due to lacking full physical models. As a practical approach for their loss estimation, the manufacturers can pre-measure the losses in standalone rigs and distribute the "loss maps" as interpolated look-up tables&#x002F;curves for the end users. However, with more factors discovered that impact the losses, e.g., DC bias and load conditions, the dimensions of the loss maps cannot be solved by conventional surface&#x002F;curve fitting methods. This paper addresses this problem by applying the Artificial Neural Network (ANN). For both inductors and transformers, Neural Network-aided loss maps (NNALMs) are designed and evaluated with comparisons against conventional loss maps to reveal the limitations of the latter caused by physically intercoupled input variables. The NNALMs not only show superior accuracy throughout the whole datasets, but also enable the loss maps to expand the dimensions to account for more factors (e.g., load conditions in transformers) and generate multiple outputs (e.g., both the winding loss and core loss). The ANN-aided loss maps can be distributed as digitized datasheets of standardized magnetics, enabling rapid, accurate and user-friendly loss estimations for power electronics engineers.

Artificial Neural Network Aided Loss Maps for Inductors and Transformers