Improving induction motor modeling accuracy: speed-dependent parameter estimation using manufacturer data

Abstract Precise modeling of induction motors is essential for high-performance industrial applications. Traditionally, equivalent circuit with constant parameters is derived from standardized locked-rotor and no-load tests or alternative methods. However, these models exhibit limitations when calculating the electrical and mechanical quantities developed by the motor at low speeds, leading to significant inaccuracies. This work presents an innovative methodology for estimating the equivalent circuit parameters of induction machines, considering the variation of rotor reactance and resistance with speed. Rather than aiming to identify the exact physical parameters, the method seeks to obtain parameter values that accurately reproduce the motor input and output variables over the entire speed range. The proposed approach is non-intrusive, relying exclusively on the manufacturer’s catalog data. The methodology consists of three steps: (i) application of an established method from the literature for traditional constant-parameter estimation; (ii) formulation of an optimization problem to address the full speed range; and (iii) a nonlinear combination of affine functions using switching functions, following the logic of Takagi-Sugeno fuzzy inference, to produce accessible mathematical expressions for the equivalent circuit under any speed/slip condition. To evaluate the effectiveness of the methodology, the torque and current curves obtained from the model are compared to data provided by manufacturers and established literature. Parameter estimation results for nine induction motors, obtained from different manufacturers, are reported. A mean error of $$10^{-2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> % was obtained for most of the analyzed motors, indicating good overall accuracy.