Improved Jiles-Atherton Model for Accurate Hysteresis Loop Prediction under Deep Saturation Conditions

초록

This paper presents an improved dynamic JilesAtherton (IDJA) hysteresis model designed to enhance parameter identification accuracy and improve optimization convergence under deep magnetic saturation. Model parameters are estimated through a genetic algorithm-based global search approach. Experimental validation is performed on a non-grain-oriented electrical steel ring sample at a peak AC flux density of Bac=1.75T and excitation frequencies of 200 Hz and 1200 Hz. The proposed IDJA model achieves a 27% reduction in the convergence index and a 43% decrease in ironloss prediction error compared to the conventional JilesAtherton model. © 2025 Korean Institute of Electrical Engineers Electrical Machinery and Energy Conversion Systems Society.

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