Simulation of iron losses in induction machines using an iron loss model for rotating magnetization loci in no electrical steel

Purpose Various iron loss models can be used for the simulation of electrical machines. In particular, the effect of rotating magnetic flux density at certain geometric locations in a machine is often neglected by conventional iron loss models. The purpose of this paper is to compare the adapted IEM loss model for rotational magnetization that is developed within the context of this work with other existing models in the framework of a finite element simulation of an exemplary induction machine. Design/methodology/approach In this paper, an adapted IEM loss model for rotational magnetization, developed within the context of the paper, is implemented in a finite element method simulation and used to calculate the iron losses of an exemplary induction machine. The resulting iron losses are compared with the iron losses simulated using three other already existing iron loss models that do not consider the effects of rotational flux densities. The used iron loss models are the modified Bertotti model, the IEM-5 parameter model and a dynamic core loss model. For the analysis, different operating points and different locations within the machine are examined, leading to the analysis of different shapes and amplitudes of the flux density curves. Findings The modified Bertotti model, the IEM-5 parameter model and the dynamic core loss model underestimate the hysteresis and excess losses in locations of rotational magnetizations and low-flux densities, while they overestimate the losses for rotational magnetization and high-flux densities. The error is reduced by the adapted IEM loss model for rotational magnetization. Furthermore, it is shown that the dynamic core loss model results in significant higher hysteresis losses for magnetizations with a high amount of harmonics. Originality/value The simulation results show that the adapted IEM loss model for rotational magnetization provides very similar results to existing iron loss models in the case of unidirectional magnetization. Furthermore, it is able to reproduce the effects of rotational flux densities on iron losses within a machine simulation.

Iron Loss Quantification in the Aim of the Estimation of Eddy Currents in Clamping Devices

In high power electrical machines, the leakage magnetic flux due to end windings induces eddy currents in clamping devices. However, it is quite difficult to quantify these losses. In order to study the effect of different clamping materials and the impact of the magnetization direction, an experimental mock-up composed of a stator and a clamping plate has been developed. An axial coil generates a circumferential magnetic flux in the stator core at different frequencies. Eddy current losses in the clamping plates are deduced from a power balance by subtracting Joule losses and iron losses from the total measured losses. Iron losses are deduced from 3D FE calculations while the impact of the frequency on B(H) curve is taken into account. Losses in the clamping device are then analyzed depending on experimental parameters.

Design and Thermal Modeling of Modular Hybrid Excited Double-Sided Linear Flux Switching Machine

This paper presents a Hybrid Excited Double-Sided Linear Flux Switching Machine (HEDSLFSM) with a crooked tooth modular stator. Generally, the conventional stators are made of a full-length iron core, increasing manufacturing costs and iron losses. Higher iron losses result in lower efficiency and lower overall performance. A U-shaped modular stator with a crooked tooth is used to lower iron consumption and increase the machine’s efficiency. Ferrite magnets are used to replace rare earth magnets, which also reduces the machine cost. Two DC excitation windings are used above and below the ferrite magnet to reduce the PM volume. 2D electromagnetic performance analysis is done to observe the key performance indices. Geometric optimization is used to optimize the Split Ratio (S.R), DC winding slot area (DCw), and AC winding slot area (ACw). Stator Tooth Width (STW), space between the modules (S.S.), and crooked angle (α) are optimized through JMAG in-built Genetic Algorithm (G.A.) optimization. High thrust force density and modular stator make it a good candidate for long-stroke applications like railway transits. The thermal analysis of the machine is performed by FEA analysis and then validated by 2D LPMC (Lumped Parametric Magnetic Equivalent Circuit) model. Both analyses are compared, and an error percentage of less than 4% is achieved.

Generic modeling, identification and optimal feedforward torque control of induction machines using steady-state machine maps

<div><div><div><div><p>A not yet available look-up table (LUT) based optimal feedforward torque control (OFTC) method for squirrel- cage induction machines (SCIMs) is presented. It is based on: (i) a generic transformer-like machine model in an arbitrarily rotating (d,q)-reference frame, considering nonlinear flux linkages and iron losses in the stator laminations; (ii) machine identification by evaluating steady-state measurements over a grid of (d,q) stator currents, producing frequency-dependent machine maps for e.g. flux linkages, torque, iron resistance and efficiency; and (iii) numerical optimization and extraction of OFTC look- up tables for optimal stator current references depending on reference torque and electrical frequency. In order to increase reproducibility, a feedback temperature controller is employed to keep the stator winding temperature constant. Moreover, throughout the identification, the electrical frequency is kept con- stant (per data set) by adapting the machine speed accordingly using a speed-controlled prime mover; this way the impact of iron losses becomes more balanced than for constant speed operation. The presented measurement results confirm that compared to constant flux operation or scalar V/Hz control, efficiency can be increased particularly in part-load operation by up to 7 %.</p></div></div></div></div>

Grain Size Influence on the Magnetic Property Deterioration of Blanked Non-Oriented Electrical Steels

Non-oriented electrical steel sheets are applied as a core material in rotors and stators of electric machines in order to guide and magnify their magnetic flux density. Their contouring is often realized in a blanking process step, which results in plastic deformation of the cut edges and thus deteriorates the magnetic properties of the base material. This work evaluates the influence of the material’s grain size on its iron losses after the blanking process. Samples for the single sheet test were blanked at different cutting clearances (15 µm–70 µm) from sheets with identical chemical composition (3.2 wt.% Si) but varying average grain size (28 µm–210 µm) and thickness (0.25 mm and 0.5 mm). Additionally, in situ measurements of blanking force and punch travel were carried out. Results show that blanking-related iron losses either increase for 0.25 mm thick sheets or decrease for 0.5 mm thick sheets with increasing grain size. Although this is partly in contradiction to previous research, it can be explained by the interplay of dislocation annihilation and transgranular fracturing. The paper thus contributes to a deeper understanding of the blanking process of coarse-grained, thin electrical steel sheets.

Adaptation and parametrization of an iron loss model for rotating magnetization loci in NO electrical steel

Purpose The magnetic characterization of electrical steel is typically examined by measurements under the condition of unidirectional sinusoidal flux density at different magnetization frequencies. A variety of iron loss models were developed and parametrized for these standardized unidirectional iron loss measurements. In the magnetic cross section of rotating electrical machines, the spatial magnetic flux density loci and with them the resulting iron losses vary significantly from these unidirectional cases. For a better recreation of the measured behavior extended iron loss models that consider the effects of rotational magnetization have to be developed and compared to the measured material behavior. The aim of this study is the adaptation, parametrization and validation of an iron loss model considering the spatial flux density loci is presented and validated with measurements of circular and elliptical magnetizations. Design/methodology/approach The proposed iron loss model allows the calculation and separation of the different iron loss components based on the measured iron loss for different spatial magnetization loci. The separation is performed in analogy to the conventional iron loss calculation approach designed for the recreation of the iron losses measured under unidirectional, one-dimensional measurements. The phenomenological behavior for rotating magnetization loci is considered by the formulation of the different iron loss components as a function of the maximum magnetic flux density Bm, axis ratio fAx, angle to the rolling direction (RD) θ and magnetization frequency f. Findings The proposed formulation for the calculation of rotating iron loss is able to recreate the complicated interdependencies between the different iron loss components and the respective spatial magnetic flux loci. The model can be easily implemented in the finite element analysis of rotating electrical machines, leading to good agreement between the theoretically expected behavior and the actual output of the iron loss calculation at different geometric locations in the magnetic cross section of rotating electrical machines. Originality/value Based on conventional one-dimensional iron loss separation approaches and previously performed extensions for rotational magnetization, the terms for the consideration of vectorial unidirectional, elliptical and circular flux density loci are adjusted and compared to the performed rotational measurement. The presented approach for the mathematical formulation of the iron loss model also allows the parametrization of the different iron loss components by unidirectional measurements performed in different directions to the RD on conventional one-dimensional measurement topologies such as the Epstein frames and single sheet testers.

Design Optimization of Multi-Layer Permanent Magnet Synchronous Machines for Electric Vehicle Applications

This paper presents a comparison between two design methodologies applied to permanent magnet synchronous machines for hybrid and electric vehicles (HEVs and EVs). Both methodologies are based on 2D finite element models and coupled to a genetic algorithm to optimize complex non-linear geometries such as multi-layer permanent magnet machines. To reduce the computation duration to evaluate Induced Voltage and Iron Losses for a given electrical machine configuration, a new methodology based on geometrical symmetries and magnetic symmetries are used and is detailed. Two electromagnetic models have been developed and used in the design stage. The first model was the stepped rotor position finite element analysis called abc model which considered the spatial harmonics without any approximation of the waveform of flux linkage inside the stator, and the second model was based on a fixed rotor position called dq model, with the approximation that the waveform of flux linkage inside the stator was sinuous. These two methodologies are applied to the design of a synchronous machine for HEVs and EVs applications. Design results and performances are analyzed, and the advantages and drawbacks of each methodology are presented. It was found that the dq model is at least 5 times faster than the abc model with high precision for both the torque and induce voltage evaluation in most cases. However, it is not the case for the iron losses computation. The iron loss model based on dq model is less accurate than the abc model with a relative deviation from the abc model greater than 70% at high control angle. The choice of the electromagnetic model during the optimization process will therefore influence the geometry and the performances of the obtained electrical machine after the optimization.

Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation

Mathematical models of induction motor (IM) used in direct field-oriented control (DFOC) strategies are characterized by parametrization resulting from the IM equivalent circuit and model-type selection. The parameter inaccuracy causes DFOC detuning, which deteriorates the drive performance. Therefore, many methods for parameter adaptation were developed in the literature. One class of algorithms, popular due to their simplicity, includes estimators based on the model reference adaptive system (MRAS). Their main disadvantage is the dependence on other machines’ parameters. However, although typically not considered in the respective literature, there are other aspects that impair the performance of the MRAS estimators. These include, but are not limited to, the nonlinear phenomenon of iron losses, the effect of necessary discretization of the algorithms and selection of the sampling time, and the influence of the supply inverter nonlinear behavior. Therefore, this paper aims to study the effect of the above-mentioned negative aspects on the performance of selected MRAS estimators: active and reactive power MRAS for the stator and rotor resistance estimation. Furthermore, improved reduced-order models and MRAS estimators that consider the iron loss phenomenon are also presented to examine the iron loss influence. Another merit of this paper is that it shows clearly and in one place how DFOC, with the included effect of iron losses and inverter nonlinearities, can be modeled using simulation tools. The modeling of the IM and DFOC takes place in MATLAB/Simulink environment.