Efficiently simulating complex hysteresis loops of soft magnetic materials under non-sinusoidal excitation based on inverse hysteresis models is currently a key research and challenge in the international electromagnetic field research. Recent research has demonstrated that the continuous inverse Play hysteresis model can accurately simulate various hysteresis behaviors

including such complex patterns. However

its existing parameter identification methods require extensive measured data and involve cumbersome processes

hindering the application and promotion of this hysteresis model. To address this issue

this paper introduces a first-order reversal curve numerical generation method and genetic algorithm

transforming the parameter identification problem of this model into a parameter optimization problem for the first time. Consequently

only three measured symmetric hysteresis loops under high

medium

and low magnetic flux densities are needed to quickly extract its parameters. Subsequently

based on the proposed parameter extraction method and this model

the hysteresis loops under sinusoidal excitation and complex hysteresis loops under non-sinusoidal excitation of soft magnetic materials are simulated respectively. These results are compared with the measured ones

verifying the high efficiency of this method and the promising application prospects of this hysteresis model.