Simulation Method for Hysteresis and Loss Characteristics of Magnetic Powder Cores Considering the Spatial Eddy Current Distribution Characteristics at the Particle Scale
ZHAO Xiaojun, ZHU Wei, AN Bo, et al. Simulation Method for Hysteresis and Loss Characteristics of Magnetic Powder Cores Considering the Spatial Eddy Current Distribution Characteristics at the Particle Scale[J]. 2026, 46(18): 7015-7026.
ZHAO Xiaojun, ZHU Wei, AN Bo, et al. Simulation Method for Hysteresis and Loss Characteristics of Magnetic Powder Cores Considering the Spatial Eddy Current Distribution Characteristics at the Particle Scale[J]. 2026, 46(18): 7015-7026.DOI: 10.13334/j.0258-8013.pcsee.251026.
The particle properties of magnetic powder core materials make the analysis of their eddy current hysteresis and loss characteristics extremely difficult. Therefore
in this paper
considering the feature that the magnetic powder core is made of metal spherical particles
the eddy current loss is divided into the eddy current loss within the particles and the eddy current loss between the particles. For the internal eddy currents of particles
the expression of the internal eddy current field of spherical particles is derived based on Maxwell's equations. For the eddy currents between particles
the method of equispaced segmentation is adopted
and the influence of the skin effect is taken into account. The distribution of the eddy current field on the cross-section of each magnetic ring segment is solved successively. The eddy current path conduction coefficient is defined to obtain the expression of the eddy current field between particles. Furthermore
based on the consistency of field separation and loss separation techniques
a high-frequency dynamic hysteresis model in the form of field separation is established
thereby achieving the prediction of core loss of granular magnetic powder core materials under high-frequency excitation. Finally
by building a comprehensive magnetic property measurement platform and coMParing the experimental measured values with the predicted values of the model
the accuracy and universality of the model prediction in this paper are verified.