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The cutting process of the electric machine laminations causes residual mechanical stress in the soft magnetic material. A local magnetic deterioration can be observed and the resulting local and global iron losses increase. A continuous local material model for the consideration of the changing magnetization properties has been introduced in a previous work as well as an a priori assessment of iron losses. A local iron loss calculation considering both a local magnetization and local loss parameters misses yet. The purpose of this study is to introduce a local iron loss calculation model considering both a local magnetization and local loss parameters.

In this paper, an approach for local iron loss simulation is developed and a comparison to the cut-edge length-dependent loss model is given. The comparison includes local loss distribution in the lamination as well as the impact on the overall motor efficiency and vehicle range in an electric vehicle driving cycle.

For an analysis of the resulting local iron loss components, both the local magnetization and iron loss parameters must be considered using physically based models. Consistently, a local iron loss model is presented in the work. The developed model can be used to gain detailed information of the local loss distribution inside the machine. The comparability of this local iron loss with the cut-edge length approach for overall system characteristics, e.g. efficiency or driving range, is shown.

A local iron loss simulation approach is a physical accurate model to describe the influence of cutting techniques on electric machine characteristics. A comparison with the less complicated a priori assessment gives detailed information about the necessity of the local model under consideration of the given problem.

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.

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.

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.

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.

The purpose of this paper is to find out how to model iron losses in electrical machines accurately and efficiently.

The starting point was a previously developed vector hysteresis model that was designed and incorporated into the 2D time‐stepping finite‐element (FE) simulation of induction machines. The developed approach here is a decoupling between the vector hysteresis model and the 2D FE model of the machine. The huge time consumption of the incorporated hysteresis model required some new approach to make the model computationally efficient. This is dealt with through an a posteriori use of the vector hysteresis model.

In this research, it was found that the vector hysteresis model, although used in an a posteriori scheme is able to accurately predict the iron losses as far as these losses are small enough not to affect the other operation characteristics of the machine.

The research methods reported in this paper deal mainly with induction machines. The methods should be applied for transient operations of the induction machines as well as for other types of machines. The fact that the iron losses do not affect very much the operation characteristics of the machine is based on the fact that the air gap field plays a major role in these machines. The method cannot be applied to other magnetic devices where the iron losses are the main loss component.

The paper is of practical value for designers of electrical machines, who use FE programs. The methods presented here allow them to use a different FE package to simulate the machine and own routines (based on the presented methods) to predict the iron losses without loss of accuracy and in a reasonably short time.

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.

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 B_m, axis ratio f_Ax, angle to the rolling direction (RD) θ and magnetization frequency f.

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.

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.

This paper presents a comparative study of different stator-segmentation topologies of a permanent magnet synchronous machine (PMSM) used in traction drives and their effect on iron losses. Using stator segmentation allows one to achieve more significant copper fill factors, resulting in increased power densities and efficiencies. The segmentation of the stators creates additional air gaps and changes the soft magnetic material’s material properties due to the cut edge effect. The aim of this paper is to present an in-depth analysis of the influence of stator segmentation on iron losses. The main goal was to compare various segmentation methods under equal excitation conditions in terms of their influence on iron loss.

A transient finite element method analysis combined with an extended iron-loss model was used to evaluate discussed effects on the stator’s iron losses. The workflow to obtain a homogenized airgap length accounting for cut edge effects was established.

The paper concludes that the segmentation in most cases slightly decreases the iron losses in the stator because of the overall reduced magnetic flux density B due to the additional air gaps in the magnetic circuit. An increase of the individual components, as well as total power loss, was observed in the Pole Chain segmentation design. In general, segmentation did not change the total iron losses significantly. However, different segmentation methods resulted in the different distortion of the magnetic field and, consequently, in different iron loss compositions. The analysed segmentation methods exhibited different iron loss behaviour with respect to the operation points of the machine. The final finding is that analysed stator segmentations had a negligible influence on the total iron loss. Therefore, applying segmentation is an adequate measure to improve PMSMs as it enables, e.g. increase of the winding fill factor or simplifying the assembly processes, etc.

The influence of stator segmentation on iron losses was analysed. An in-depth evaluation was performed to determine how the discussed changes influence the individual iron loss components. A workflow was developed to achieve a computationally cheap homogenized model.

Pulse width modulated (PWM) inverters are widely used to feed induction motors for variable speed applications. The use of PWM power supplies induces additional magnetic losses in the magnetic circuit of the electrical machine. The aim of this paper is to present a novel analytical approach to account for these losses.

The methodology proposed here consists in identifying the analytical method with a static Preisach hysteresis model. The Preisach model was validated by comparing it with measurements obtained from an Epstein frame. Then, the results obtained with this approach were compared with a basic analytical method that is widely used.

The authors' model provides a fast way for estimating the minor loop iron losses introduced by static convertors. They compared the proposed model with another analytical model (J. Lavers model) for different wave forms. One can observe that the J. Lavers model overestimates the iron losses introduced by the non‐centred minor loops.

In this paper, an improved analytical model is presented which estimates the non‐centred minor loop iron losses. In order to do a precise estimation of the iron loss introduced by the minor loops, the authors' model takes into account the position and the size of the minor loop. The proposed model is identified from a static Preisach model.

To take account of the uncertainties introduced on the magnetic properties during the manufacturing process, the present work aims to focus on the stochastic modelling of iron losses in electrical machine stators.

The investigated samples are composed of 28 slinky stators, coming from the same production chain. The stochastic modelling approach is first described. Thereafter, the Monte‐Carlo sampling method is used to calculate, in post‐processing, the iron loss density in a PMSM that is modelled by the finite element method.

The interest of such an approach is emphasized by calculating the main statistical characteristics associated to the losses variability, which are Gaussian distributed for A and Ω formulations.

The originality of the approach is due to the fact that the global influence of the manufacturing process (cutting, assembly, …) on magnetic properties of the considered samples is taken into account in the way of computing the iron losses.

This paper aims to establish a modified variable coefficient calculation model to analyse the control parameter effect on the iron loss of switched reluctance motor under pulse width modulation (PWM) mode.

The finite element model is solved to get the flux density by python language. Due to non-sinusoidal flux density feature and the effect of PWM excitation, the Fourier transform is applied in consideration of harmonic components. To improve the accuracy of iron loss computation, the effect of minor loops is considered by using the rain-flow counting method.

When the speed fluctuates around the set speed and the fluctuations are relatively small, it is useful to reduce the iron loss with smaller duty ratio and turn-on angle or greater duty ratio and smaller turn-off angle. The iron loss is less affected by chopping frequency, while the iron loss increases obviously with higher conduction angles. The iron loss under non-energy-returnable-voltage-chop mode is greater than energy-returnable-voltage-chop mode.

The modified variable coefficient MIEM5 iron loss model is proposed to improve the accuracy of iron loss calculation. Then the control parameters such as duty ratio, chopping frequency, turn on angle and turn off angle are analysed under PWM mode.

The losses incurred in ferromagnetic materials under PWM excitations must be predicted accurately to optimize the design of modern electrical machines. The purpose of this paper is to employ mathematical hysteresis models (i.e. classical Preisach model) to predict iron losses in electrical steels under PWM excitation without compromising the computational complexity of the model.

In this paper, a novel approach based on the dynamic inverse Preisach model is proposed to model the iron losses. The PWM magnetic flux density waveform is decomposed into its harmonic component using Fourier series and a weighted Everett function is computed based on these harmonic components. The Preisach model is applied for the given flux waveform and results are validated against the measurements.

The paper predicts the total iron loss by computing a weighted Everett function based on the harmonics present in PWM waveform. Moreover, it formulates the possibility of utilizing the classical Preisach model to predict iron losses under PWM excitation.

The approach is still limited in terms of its application at high frequencies. This work may eventually lead toward the accurate prediction of iron loss under PWM excitation in electromagnetic machine design.

The paper provides a simple approach applying the Preisach model for the prediction of iron losses under PWM excitation. The proposed approach does not require additional experimental data beyond B-H loops measured under sinusoidal excitation.

A novel approach is presented to incorporate the frequency dependence into a static inverse Preisach model. The approach extends the ability of the static Preisach model to compute total iron loss under PWM excitation using a weighted Everett function.

Due to the increasing amount of high power density high-speed electrical machines, a detailed understanding of the consequences for the machine’s operational behaviour and efficiency is necessary. Magnetic materials are prone to mechanical stress. Therefore, this paper aims to study the relation between the local mechanical stress distribution and magnetic properties such as magnetic flux density and iron losses.

In this paper, different approaches for equivalent mechanical stress criteria are analysed with focus on their applicability in electrical machines. Resulting machine characteristics such as magnetic flux density distribution or iron are compared.

The study shows a strong influence on the magnetic flux density distribution when considering the magneto-elastic effect for all analysed models. The influence on the iron loss is smaller due to a high amount of stress-independent eddy current loss component.

The understanding of the influence of mechanical stress on dimensions of electrical machines is important to obtain an accurate machine design. In this paper, the discussion on different equivalent stress approaches allows a new perspective for considering the magneto-elastic effect.