Magnetic components are responsible for a significant portion of the losses in switched-mode power converters, and predicting these losses is vital to a successful design process. Core losses specifically are notoriously hard to predict, and many standard modeling techniques are outdated and inaccurate. This paper presents a novel approach that integrates an existing, equation-based, core loss algorithm with a simple random forest regression model to provide increased accuracy in core loss predictions without incurring significant computational costs. The regression model uses the equation-based model as a starting point and attempts to predict and correct the error through a multiplicative correction coefficient. The approach is trained and validated using the MagNet database of experimentally measured core loss data that includes a wide variety of materials and operating conditions. Results are presented that show the hybrid model reduces errors to an average of less than one-quarter of what they were when using conventional, equation-based techniques.
