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Non-oriented electrical steel presents anisotropic behaviour. Modelling such anisotropic behaviour has become a necessity for accurate design of electrical machines. The main aim of this study is to model the magnetic anisotropy in the non-oriented electrical steel sheet of grade M400-50A using a phenomenological hysteresis model.

The well-known phenomenological vector Jiles–Atherton hysteresis model is modified to correctly model the typical anisotropic behaviour of the non-oriented electrical steel sheet, which is not described correctly by the original vector Jiles–Atherton model. The modification to the vector model is implemented through the anhysteretic magnetization. Instead of the commonly used classical Langevin function, the authors introduced 2D bi-cubic spline to represent the anhysteretic magnetization for modelling the magnetic anisotropy.

The proposed model is found to yield good agreement with the measurement data. Comparisons are done between the original vector model and the proposed model. Another comparison is also made between the results obtained considering two different modifications to the anhysteretic magnetization.

The paper presents an original method to model the anhysteretic magnetization based on projections of the anhysteretic magnetization in the principal axis, and apply such modification to the vector Jiles–Atherton model to account for the magnetic anisotropy. The replacement of the classical Langevin function with the spline resulted in better fitting. The proposed model could be used in the numerical analysis of magnetic field in an electrical application.

The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model.

In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated.

The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model.

This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.

The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function.

The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops.

Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.

Owing to the excellent performance, giant magnetostrictive materials (GMMs) are widely used in many engineering fields. The dynamic Jiles–Atherton (J-A) model, derived from physical mechanism, is often used to describe the hysteresis characteristics of GMM. However, this model, despite cited by many different literature studies, seems not to possess unique expressions, which may cause great trouble to the subsequent application. This paper aims to provide the rational expressions of the dynamic J-A model and propose a numerical computation scheme to obtain the model results with high accuracy and fast speed.

This paper analyzes different published papers and provides a reasonable form of the dynamic J-A model based on functional properties and physical explanations. Then, a numerical computation scheme, combining the Newton method and the explicit Adams method, is designed to solve the modified model. In addition, the error source and transmission path of the numerical solution are investigated, and the influence of model parameters on the calculation error is explored. Finally, some attempts are made to study the influence of numerical scheme parameters on the accuracy and time of the computation process. Subsequently, an optimization procedure is proposed.

A rational form of the dynamic J-A model is concluded in this paper. Using the proposed numerical calculation scheme, the maximum calculation error, while computing the modified model, can remain below 2 A/m under different model parameter combinations, and the computation time is always less than 0.5 s. After optimization, the calculation speed can be enhanced with the computation accuracy guaranteed.

To the best of the authors’ knowledge, this paper is the first one trying to provide a rational form of the dynamic J-A model among different citations. No other research studies focus on designing a detailed computation scheme targeting the fast and accurate calculation of this model as well. And the performance of the proposed calculation method is validated in different conditions.

In this communication, the Preisach and Jiles‐Atherton models are studied to take hysteresis phenomenon into account in finite element analysis. First, the models and their identification procedure are briefly developed. Then, their implementation in the finite element code is presented. Finally, their performances are compared with an electromagnetic system made of soft magnetic composite. Current and iron losses are calculated and compared with the experimental results.

Although the original Jiles‐Atherton (J‐A) hysteresis model is able to represent a wide range of major hysteresis loops, in particular those of soft magnetic materials, it can produces non‐physical minor loops with its classical equations. The purpose of this paper is to show a modification in the J‐A hysteresis model in order to improve the minor and inner loops representation. The proposed technique allows the J‐A model representing non‐centred minor loops with accuracy as well as improving the symmetric inner loops representation.

Only the irreversible magnetization component is slightly modified keeping unchanged the other model equations and the model simplicity. The high‐variation rate of the irreversible magnetization, which causes the non‐physical behaviour of minor loops, is limited by introducing a new physical parameter linked to the losses. Contrarily to other modifications of the original model found in the literature, the previously knowledge of the magnetic field waveform is not needed in this case.

The modified hysteresis model is validated by comparison with experimental results. A good agreement is observed between calculations and measurements. The modified model retains the low‐computational effort and numerical simplicity of the original one.

This paper shows that a classical scalar hysteresis model can be suitably used to take into account the minor loops behaviour and be included in a finite element code. The methodology is useful for the design and analysis of electromagnetic devices under distorted flux patterns.

Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops.

A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process.

The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops.

The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.

The purpose of this paper is to combine the Jiles-Atherton (J-A) hysteresis model with the field separation approach to realize the accurate simulation of dynamic magnetostrictive characteristics of silicon steel sheet.

First, the energy loss of silicon steel sheet is divided into hysteresis loss Why, classical eddy current loss Wed and anomalous loss Wan according to the statistical theory of losses. The Why is calculated by static J-A hysteresis model, Wed and Wan are calculated by the analytical formulae. Then, based on the field separation approach, the dynamic magnetic field is derived. Finally, a new dynamic magnetostrictive model is proposed by means of the quadratic domain rotation model.

Comparison of simulation and experimental results verifies that the proposed model has high accuracy and strong universality.

The proposed method improves the existing method’s problem of relying on too much experimental data, and the method ensures the calculation accuracy, parameter identification accuracy and engineering practicability. Consequently, the presented work greatly facilitates further explorations and studies on simulation of dynamic magnetostrictive characteristics of silicon steel sheet.

Classically the magnetic material models are considered with a deterministic approach. Nevertheless, when submitted to the fabrication process, the magnetic core properties are negatively impacted and may be subject to variability during the process. This variability can be of such importance that the performances of the final device (electrical machine) will also present a noticeable variability. The aim of this research is to develop a stochastic model of the magnetic behaviour law of slinky stators used in claw pole generators. The proposed methodology is general and can be applied to other physical properties of electrical devices.

The approach is based on a methodology that uses experimental data and a statistical description of the magnetic properties. To that end, a set of samples issued from the same chain of assembly is considered. The hysteresis model is then developed by accounting for the parameter correlation structure.

It is found that the magnetic hysteresis properties of the studied samples can be modelled by means of statistical tools applied to the parameters of the hysteresis model. The dependency of the parameters can also be accounted for a more accurate modelling.

The paper proposes a statistical approach and a methodology that are applied to the hysteresis modelling accounting for the variability of the magnetic properties. The developed model can be further used in a numerical tool to represent the impact on the performances of electrical devices that are subject to the fabrication process variability.

This paper aims to compare different static history-independent hysteresis models (mathematical-, behavioural- and physical-based ones) and a history-dependent hysteresis model in terms of parameter identification effort and accuracy.

The discussed models were tested for distorted-excitation waveforms to explore their predictions of complex magnetization curves. Static hysteresis models were evaluated by comparing the calculated and measured major and minor static hysteresis loops.

The analysis shows that the resulting accuracy of the different hysteresis models is strongly dependent on the excitation waveform, i.e. smooth excitations, distorted flux waveforms, transients or steady-state regimes. Obtained results show significant differences between predictions of discussed static hysteresis models.

The general aim was to identify the models on a very basic and limited set of measured data, i.e. if possible using only the measured major static loop of the material. The quasi-static major hysteresis loop was measured at B_max = 1.5 T.

The presented analysis allows selection of the most-suited hysteresis model for the sought-for application and appraisal of the individual limitations.

The presented analysis shows differences in intrinsic mechanisms to predict magnetization curves of the majority of the well-known static hysteresis models. The results are essential when selecting the most-suited hysteresis model for a specific application.