Characterization and Modeling of Soft Magnetic Materials for Improved Estimation of PWM-Induced Iron Loss

This paper investigates the iron loss properties of soft magnetic materials under conditions of high excitation frequency and varying pre-magnetized dc-bias fields (i.e., different magnetization states). Using measured iron loss data collected using customized test equipment, a modified dynamic Jiles-Atherton model has been developed specifically for the purpose of achieving improved PWM-induced iron loss estimation. The accuracy and scalability of the proposed prediction model for different conditions are evaluated including the flux ripple amplitude, excitation frequency, dc-bias field, and duty cycle of the triangular waveform. Experimental results have confirmed that the proposed model can accurately predict the dynamic hysteresis loop and corresponding iron loss over a wide range of operating conditions. The model parameters derived from a limited number of tests can be used to predict the iron loss in a much broader operating range, which makes it a promising tool for PWM-induced iron loss estimation in new machine designs intended for demanding operating conditions.