Iron Loss Model Research Articles

Characterization and Modeling of Soft Magnetic Materials for Improved Estimation of PWM-Induced Iron Loss

This article investigates the iron loss properties of soft magnetic materials under conditions of high excitation frequency and varying premagnetized dc-bias fields (i.e., different magnetization states). Using measured iron loss data collected from the customized test equipment, a modified dynamic Jiles–Atherton model has been developed specifically for the purpose of achieving improved pulsewidth modulation (PWM)-induced iron loss estimation. The accuracy and scalability of the proposed prediction model for different conditions are evaluated including the flux ripple amplitude, excitation frequency, dc-bias field, and symmetry of the triangular waveform. Experimental results have confirmed that the proposed model can accurately predict the dynamic hysteresis loop and corresponding iron loss over a wide range of operating conditions. By introducing a companion iron loss model developed for fundamental and low-order harmonic fields, the total iron loss under inverter PWM voltage excitation is explored. The results show that model parameters derived from a limited number of tests can be used to predict the iron loss in a much broader operating range, which makes it a promising tool for PWM-induced iron loss estimation in new machine designs intended for demanding operating conditions.

An Analytical Iron Loss Calculation Model of Inverter-Fed Induction Motors Considering Supply and Slot Harmonics

A fast and accurate analytical inverter-fed induction machine iron loss calculation model is proposed in this paper. The proposed model takes account of the influence of the output voltage harmonics from the inverter on the iron loss of the motor based on the piecewise variable coefficients method. Additionally, the proposed method incorporates slot harmonic component's influence on the iron loss. The validity of the proposed model is verified by comparing its calculated core loss values with measured ones of a 5.5- and a 55-kW inverter-fed induction motors under different speeds and load conditions. Compared with the classical iron loss model and piecewise variable coefficient iron loss model based on the finite-element method, the proposed model can reduce the computational burden significantly with desirable accuracy.

Magnetic Loss Analysis in Coaxial Magnetic Gears

This paper proposes a procedure for computing magnetic losses in coaxial magnetic gears. These magnetic structures are made of permanent magnets and ferromagnetic poles in relative motion transferring torque between two shafts in a contactless way. The loss computation in magnetic materials is crucial to define the system performance. The flux distribution inside the iron parts is computed by means of the finite element method and a model of iron losses taking into account the rotational nature of the flux loci is applied. The procedure highlights where the major loss sources are present and gives the opportunity to evaluate some corrective measures to reduce their effects. Particular attention is devoted to the 2D modeling in presence of permanent magnets segmentation.

Iron-loss model for arbitrary magnetization loci in NO electrical steel

The design of electrical machines requires accurate models for the estimation of locally occurring iron losses. For this purpose, varieties of iron-loss models are proposed. These allow one to calculate iron losses as a function of the unidirectional

A New Hysteresis Loss Estimation in the Induction Motor Core Considering Rotating Magnetic Fields

A rotating machine such as inductor motor has highly distorted elliptical magnetic flux densities in its stator core. The iron loss estimation taking into account the rotating magnetic field is significant not only for the design but also for the performance analysis of electrical machines. A rotational hysteresis loss formula is also proposed to improve the computation accuracy especially when the magnetic flux density is high or the axis ration of a rotational magnetic flux density is big. For an induction motor core model, iron losses are experimentally measured on the top sheet of the core by using a self-developed B–H vector sensor. The proposed iron loss model is validated through comparisons of the calculated iron losses with experimentally measured ones.

Iron loss simulation using a local material model

PurposeThe 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.Design/methodology/approachIn 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.FindingsFor 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.Originality/valueA 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.

Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced.

Iron Loss Analysis of Double-Sided Linear Switched Reluctance Launcher

The iron loss of doubled-sided linear switch reluctance motor (DLSRM) is analyzed and calculated in this paper. Because of the double salient structure of the switched reluctance motor and the nonlinearity of the magnetization curve of the ferromagnetic material, the flux density waveform is nonsinusoidal. So, it is difficult to calculate the iron loss of DLSRM. This paper mainly introduces the calculation method of dynamic magnetic density of the DLSRM, and basing on this, the simulation model of iron loss of DLSRM is built and compared the iron loss in different parts of the total as a percentage of the motor. Finally, an experimental platform based on DLSRM is built to measure the actual iron loss in the motor and verify the correctness of the calculation of iron loss.

Influence of DC-Biased Magnetic Induction on Magnetic Property of Silicon Steel

In this paper, the magnetic properties of non-oriented (NO) and grain-oriented (GO) silicon steels with dc-biased induction are measured and analyzed within the wide range of 20–1000 Hz, 0–1.9 T. While by the physical method, we found that all the ac-, dc-, and dc-biased fields do not influence the electrical resistivity of silicon steel, and hysteresis loss is mainly responsible for the iron loss increase when dc-biased induction exists. With the frequency and ac induction going up, the effect of dc-biased induction on iron loss will become weak. Compared with NO steel, iron loss of GO steel is more sensitive to dc-biased induction. The validation shows that existing iron loss models which claim to consider dc-biased magnetic induction are not satisfactory up to now when applied in the wide range of dc-biased induction, ac induction, and frequency.

Time‐domain computation of rotational iron losses considering the bulk conductivity for PMSMs

This study investigates an advanced finite-element (FE) technique for the evaluation of rotational iron losses based on a time-domain computation, where the bulk conductivity of the core materials is considered. The iron-loss characteristics are discussed for a radial flux permanent magnet synchronous machine (PMSM) for a wind generation application, with closed slots and outer rotor topology. The following factors are taken into account: (i) a real-time rotational iron-loss computation; (ii) the bulk conductivity of the steel laminations; and (iii) the influence of the controller harmonics on the system during transient conditions. The magnetic induction vector locus of each iron component is also discussed, where the magnetic induction is numerically modelled [three-dimensional (3D) finite element analysis (FEA)], computed using multiple magnetic antennae and is also experimentally verified. This comparative study shows a torque–frequency-loss computation that is presented from low to high frequencies (50–800) Hz. The FE model of the total iron losses for the PMSM using both pure sinusoidal and proportional–integral pulse-width modulation currents is studied and experimentally verified on a surface-mounted PMSM. The proposed method of iron losses prediction significantly reduced the rate of error between 3D FEA and experimental data to 1.7%.

Evaluation of Iron Loss Models in Electrical Machines

In this paper, more than 10 different iron loss models are experimentally evaluated, which cover alternating and rotating fields, the influence of temperature, dc bias flux density and distorted flux density due to pulsewidth modulation inverter. Iron loss models considering alternating fields are evaluated by the measured results of a lamination ring specimen. The iron loss model considering the rotating field and the nonsinusoidal field are evaluated by the measured results of an electrical machine under different conditions. The iron loss models considering temperature influence are also evaluated by thermal analyses and experimental tests. Based on these comprehensive investigations, the iron loss models having the best prediction accuracy for each case are identified.

Development of a Hybrid Analytical Model for a Fast Computation of Magnetic Losses and Optimization of Coaxial Magnetic Gears

This paper focuses on a two-dimensional (2-D) analytical model for a fast computation of magnetic losses due to eddy currents in the permanent magnets as well as iron losses in the ferromagnetic parts within coaxial magnetic gears. The magnetic field distribution is computed in yokes and permanent magnets by solving both Maxwell's equations, whereas for pole pieces, the magnetic field is computed by coupling the previous analytical model with a reluctance network model. Both the eddy current losses and iron losses are determined from this hybrid analytical model. The iron loss model takes into account the temporal and spatial variations of flux density. The eddy current loss model takes into account the magnet splitting. Results of this magnetostatic eddy current loss model are then compared to the results obtained with a 2-D magnetodynamic finite element model. A verification of validity limits is also proposed. The final function of this analytical model is to ensure integration into a set of models in the aim of a global mechatronic optimization of magnetic gears, for their insertion into multimegawatt wind turbines. A preliminary bi-objective, mass-efficiency optimization protocol is subsequently proposed along with an analysis of the computation time reduction via the presented models.

Prediction and experiment of DC- bias iron loss in radial magnetic bearing for a small scale turbomolecular pump

Iron loss calculation is a crucial part of turbomolecular pump design, since the molecular pump works in vacuum environment and the iron loss eventually transforms into thermal radiation. In this paper, an iron loss prediction model for a radial magnetic bearing of turbomolecular pump considering temperature influence is proposed. A ring specimen test under different dc-bias current, different frequencies, flux densities and temperatures are used to measure iron loss of steel laminations. Then, an iron loss model with variable hysteresis loss coefficients, eddy-current loss coefficients and dc-bias loss coefficients is used for iron loss calculation of the magnetic bearing. Flux densities of different part of magnetic bearing under different working states are obtained by using FEM. Finally, the measurement and subtraction method of iron loss in radial magnetic bearing is proposed, the accuracy the proposed iron loss model is validated by an iron loss test on a prototype of a 300 L/s 36000 r/min turbomolecular pump supported by magnetic bearing.

The Iron Loss Analysis of Direct-driving External-rotor Motor with Cobalt-iron Alloy

In this paper, the iron loss characteristics of an External Rotor Permanent Magnet Synchronous Motor (ER-PMSM) for direct-driving of electric vehicle is analyzed in detail. As a kind of cobalt-iron alloy, whose saturation magnetic density is about 2.4 Tesla, is adopted to manufacture the iron core, the experimental platform has been established to test the B-H curve and the iron loss characteristics of the alloy, firstly. According to the characteristics of iron loss coefficient varying with frequency, a variable coefficient orthogonal decomposition iron loss model is proposed. Then, a magnetic loss calculation model based on cobalt-iron alloy is established. The empirical coefficient of iron loss is re-corrected, and the analysis model of the iron loss is obtained. Finally, the experimental platform is set and a prototype of ERPMSM is manufactured. The iron loss of the stator with cobalt-iron alloy is tested. The experimental results agree well with the numerical results and the calculated results, which verify the correctness of the theoretical analysis.

Simulink Model for PWM-Supplied Laminated Magnetic Cores Including Hysteresis, Eddy-Current, and Excess Losses

A new implementation of an iron-loss model for laminated magnetic cores in the MATLAB/Simulink environment is proposed in this paper. The model is based on numerically solving a one-dimensional diffusion problem for the eddy currents in the core lamination and applying an accurate hysteresis model as the constitutive law. An excess loss model is also considered. The model is identified merely based on the catalog data provided by the core material manufacturer. The implementation is validated with analytical and finite-element models and experimentally in the case of a toroidal inductor supplied from a GaN FET full-bridge inverter with 5–500 kHz switching frequencies and a deadtime of 300 ns. Despite the simple identification, a good correspondence is observed between the simulated and measured iron losses, the average difference being 3.3% over the wide switching frequency range. It is shown that accounting for the skin effect in the laminations is significant, in order to correctly model the iron losses at different switching frequencies. Some differences between the measured and simulated results at high switching frequencies are also discussed. The model is concluded to be applicable for designing and analyzing laminated magnetic cores in combination with power-electronics circuits. The Simulink models are openly available.

Integration of Iron-Loss Resistance Into Dynamic Models of 2 <inline-formula> <tex-math notation="LaTeX"> $\times$</tex-math> </inline-formula> 3-Phase Hybrid-Excited Synchronous Machine

The paper focuses on the modeling of iron losses for synchronous electric machines. Specifically, the paper describes the inclusion of iron-loss resistance parallel to the main inductance of a dual-voltage alternator model. Integration of the iron loss resistance is implemented in two different models: a quadrature direct (QD) axis model and a voltage behind reactance (VBR) model. Simulations with both models are performed and compared against measurements with a machine prototype. The measurements show that the usually made assumption of constant iron loss resistance does not accurately represent the iron losses of a claw-pole alternator. Therefore, the parallel resistance is implemented also as lookup table (LUT) and derived from measurements. The presented models can be parametrized as conventional 2 $\times$ 3-phase synchronous machine with one-voltage level.

Loss Calculation and Thermal Analysis for Nine-Phase Flux Switching Permanent Magnet Machine

This paper investigates the accurate loss calculation and thermal analysis for a nine-phase flux switching permanent magnet (FSPM) machine. First, the 3 D finite element method (FEM) model is developed to calculate the PM eddy current loss, in which magnet axial segmentation is taken into account. In order to calculate the iron loss quickly and precisely, the magnetic flux leakage coefficient is proposed in this paper to modify the 2 D calculation model of iron loss. Both the dc-biased magnetic induction and the rotational magnetization are taken into account. Then, the key parameters for thermal modeling, such as the convective heat-transfer coefficient between external frame and ambient, and the coefficient of convection heat transfer in the end region, are derived, based on which a dissymmetric 3 D FEM are used for thermal calculation of nine-phase FSPM machine. Finally, experiments on a prototype machine at the rated generation operation condition are carried out to verify the effectiveness of the developed model.

No-load iron loss model for a fractional-slot surface-mounted permanent magnet motor based on magnetic field analytical calculation

The common analytical models for the no-load iron loss of permanent magnet(PM) motors usually neglect the iron loss caused by the rotating magnetic field in the tooth tips and the harmonics of the magnetic fields in the teeth and yokes. This paper presents an analytical model for no-load iron loss of a fractional-slot surface-mounted permanent magnet motor. According to the existing analytical model of the magnetic field distribution in the slotted air gap, the magnetic flux densities considering the harmonics of the stator tooth and yoke are both derived based on the continuity of magnetic flux. Due to the complexity of the magnetic field in the tooth tip, the tangential flux density of the tooth tip is approximated by an equivalent sine wave and the radial component is regarded to be the same as that of the corresponding tooth. After obtaining the magnetic fields in stator different regions, the analytical iron loss is calculated by using the Bertotti model and the orthogonal decomposition model. A 20-pole/24-slot PM synchronous motor is taken as an example. The maximum error between the analytical model and finite element model(FEM) is 5.46%, which verifies the validity of the proposed method.

Thermal-Loss Coupling Analysis of an Electrical Machine Using the Improved Temperature-Dependent Iron Loss Model

Iron loss prediction is very important for the evaluation of efficiency, temperature and demagnetization of electrical machines. The models developed in [1]–[4] are most commonly used iron loss models for electrical machine analyses. However, none of these iron loss models considers the influence of temperature, which has been experimentally confirmed in [5]. In [6], an improved iron loss model which can consider temperature dependencies of hysteresis and eddy current losses separately is developed. By applying the improved iron loss model, the temperature influence on the iron loss can be fully considered. It is then possible to couple the thermal and loss analyses with each other by utilizing the improved iron loss model, which will be the subject of this paper. The iron loss model developed in [4] is one of the most accurate iron loss models when the temperature is constant with the help of variable coefficients. The iron loss p Fe can be expressed as: Equation (1) where f is the frequency. B m is the peak value of alternating flux density. k h (f,B m ) and k e (f,B m ) are the hysteresis loss and the eddy current loss coefficients, respectively. It should be noted that the iron loss model (1) cannot consider the influence of temperature on iron loss while the temperature influences iron loss significantly. According to the iron loss model developed in [5], both the hysteresis loss k h (f,B m ) coefficient and the eddy current loss k e (f,B m ) coefficient vary not only with frequency and flux density but also with temperature. Therefore, the improved iron loss model is then developed and can be expressed as: Equation (2) where p Fe,T is the iron loss density at the actual temperature T. k h (T,f,B m ) and k e (T,f, B m ) are the hysteresis loss and eddy current loss coefficients, respectively. In order to evaluate the iron loss models in electrical machines, thermal tests and analyses are carried out in a 12-slot/10-pole IPM machine. The schematic diagram of the test system is shown in Fig. 1(a). The 12-slot/10-pole IPM machine is connected a three phase AC power source. The magnets are removed and the rotor is locked in order to eliminate magnet eddy current and mechanical losses. As shown in Fig. 1(b), four thermal couples are equipped in the electrical machine to measure the temperature at different positions, i.e. the stator tooth, the stator yoke, the rotor magnet slot and the rotor yoke. The temperatures are measured when the electrical machine is powered by the AC power source. For the temperature prediction, the iron losses are calculated by the existing model and the improved model, respectively. The thermal model of the IPM machine is also built in Motor-CAD. The thermal model is then analysed with calculated copper loss and iron losses. The predicted temperatures of the electrical machines can be obtained. Fig. 2 compares the measured and predicted results by the existing iron loss model and the improved model. It can be seen that the predicted temperatures by the existing iron loss model become inaccurate when the temperature is high. This is due to the fact that the existing iron loss model cannot consider the temperature dependency of the iron loss. The input iron loss for the thermal analysis keeps constant while the actual iron loss decreases significantly with the temperature rise. On the other hand, the predicted temperatures keep good accuracy when the temperature reaches 100 degrees Celsius or even higher. This is due to the fact that the improved iron loss model considers the temperature dependency of the iron loss. Input iron losses for the thermal analysis vary with the temperature rise. In other words, the thermal and loss analysis can be coupled with each other by utilising the improved iron loss model, which is more close to the actual condition in electrical machines. The details will be investigated and described in the full paper.

Examination of Temperature-Dependent Iron Loss Models Using a Stator Core

In order to examine the popular temperature-dependent iron loss models with unidirectional magnetization, we cut and measured a ring sample and stator core with several hundreds of combination of temperatures (RT-200 °C), frequencies (20–1 kHz), and magnetic inductions (0–1.9 T). Coefficients of various iron loss models are extracted based on the sample test, and then imported into the finite element method postprocessor to calculate the iron loss of the stator core. We found that constant coefficient loss separation temperature-dependent iron loss models can achieve a good fitting accuracy (about 5%) on ring sample in a wide range of 20–1 kHz, 0–1.9 T, RT-200 °C, while loss coefficients extracted on small ring sample could lead to 15%–20% error when predicting iron loss of larger sized stator stamp. The iron loss of silicon steel decreases with rising temperature, and thicker silicon steel is more influenced by temperature.