Electromagnetic Performance Prediction of an Induction Machine Using an Improved Permeance-Based Equivalent Circuit Model Considering the Frequency and B–H Curve Dependent Core Loss Branches
Areej Fatima, Tim Stachl, Wenlong Li, Mohammad Sedigh Toulabi, Glenn Byczynski, Jimi Tjong, Narayan C. Kar
Abstract
Equivalent circuit models (ECMs) are extensively used in the prediction of steady-state performance characteristics of electric machines due to its simplicity and ease of implementation. In this article, permeance-based ECM is developed and improved by incorporating the machine's nonlinearities, and magnetic saturation. A recursive core loss model as a function of air gap voltage, considering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B–H</i> hysteresis, eddy current, excess loss coefficients, and Steinmetz factor is developed to improve the accuracy of the electromagnetic performance prediction. Furthermore, an adaptive restart genetic algorithm is used to improve the accuracy of the coefficients and Steinmetz factor prediction over a wide range of frequencies and flux densities for both the stator and rotor. Moreover, skin effect is incorporated in the eddy current core loss to accurately consider the impact of varying frequencies and loadings. Finally, the proposed permeance-based ECM's calculated torque, core loss, and efficiency are experimentally validated using a prototyped squirrel–cage traction induction machine at various speeds and loading conditions. Toward net-zero carbon emissions, transportation electrification is a significant step requiring high–speed traction motors for electric vehicle application. Therefore, an accurate ECM is proposed that can successfully capture the frequency-dependent effects in the prediction of core loss, torque, and efficiency.