Prediction of Iron Loss Using a Hybrid Energy-Based Hysteresis Model

Abstract

An iron loss prediction method that combines the energy-based hysteresis model with the statistical theory of loss and the field separation approach is proposed. The prediction method aims to provide a more physically realistic description of magnetic energy dissipation. First, a static energy-based hysteresis model is established based on thermodynamic principles, drawing inspiration from both the Jiles-Atherton model and the Preisach model. The static model accurately predicts hysteresis loss. Subsequently, a dynamic iron loss model is developed by incorporating statistical loss theory into the static energy-based hysteresis model through the field-separation approach. Eddy current loss and excess loss are taken into account by introducing an additional field strength associated with dynamic iron losses into the total magnetic field strength. Finally, an Epstein frame testing platform is constructed to measure iron loss over a range of frequencies. Comparisons between analytical results and measured results show good consistency. The proposed model serves as a promising approach for iron loss prediction, offering a sound physical foundation and strong scalability.