Parameter Estimation and Sensorless Control of Variable Phase-Pole Induction Machines in the Context of Magnetic Pole Transition
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Date
2025-05
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Addis Ababa University
Abstract
The importance of transitioning from fossil fuel cars to electric vehicles is a critical step in the global initiative
to mitigate the impacts of climate change. As electric vehicles become a central element of sustainable
transportation systems, countries worldwide, including Ethiopia, are working to achieve their carbon reduction
goals. One of the primary reasons for this shift is the absence of tailpipe emissions from EVs, unlike traditional
internal combustion engine vehicles, which release substantial amounts of carbon dioxide 𝐶𝑂2 and other
harmful pollutants into the environment. Hence, replacing conventional cars with electric ones significantly
reduces greenhouse gas emissions, which are a major contributor to global warming.
The first part of this dissertation focuses on the necessity of accurately estimating electrical parameters for
multiphase machines to be used in advanced control approaches. This necessity is especially pertinent for the
special subclass of multiphase machines identified as Variable Phase-Pole Induction Machines (VPPIMs). This
part of the research explores, calculates, and experimentally confirms various parameter estimation techniques
for awound stator coil induction machine with independent slot current control capable of dynamically adjusting
the phase-pole configuration in real time. The parameter estimation techniques covered are a traditional finite
element analysis-based stator impedance calculation and an application of the finite element-based harmonic
plane decomposition (HPD) method. Experimental validation demonstrates the proposed estimation methods
across different machine configurations. When comparing the two methods, parameter estimation based on
HPD theory aligns closely with practical outcomes. The classical method results in a 22% relative error for the
magnetizing inductance (𝐿𝑀) and a 62% overestimation for the leakage inductance (𝐿𝜎 ) in the 3-phase/2-pole
VPPIM compared to measurements. The HPD approach reduces these errors to 15% for magnetizing inductance
and 36% for leakage inductance. The conventional approach yields a 17% rate for the magnetizing inductance
𝐿𝑀, which decreases to 6% with the HPD method. Similarly, for the leakage inductance (𝐿𝜎 ), the rate of 54% is
reduced to 23% in the 9-phase/2-pole VPPIM configuration. Additionally, the HPD model cuts computing time
to one-eighth of the conventional methods, significantly enhancing computational efficiency.
The latter portion of this research concentrates on variable phase-pole induction machines, which show
potential for electromobility applications because of their ability to electronically modify the number of magnetic
poles during operation, consequently enhancing the machine’s torque-speed performance. This research
introduces a model reference adaptive system approach for the sensorless speed operation of a nine-phase
variable phase-pole induction machine. The same adaptive full-order observer estimates mechanical speed
in two distinct ways, each corresponding to a different pole-pair configuration of interest. Sensorless drive
operation is sustained during pole-pair configuration transitions through an appropriate switch between
the two speed-estimation strategies, ensuring accurate rotor flux linkage information for high-performance
control. The proposed method’s simulation and experimental outcomes strongly correlate, affirming the sensorless
methodology’s effectiveness under variable load scenarios and during positive/negative speed transitions.
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Keywords
Electric drives, Electrical machine, Multiphase drive, Pole-phase changing, State-space model, Speed control, Sensorless control.