Prediction Of Iron Losses Research Articles

New method for determining iron power losses in switched reluctance motors with laminated and composite magnetic cores

Power losses from motors and drives can decrease their efficiency. The development of devices with high efficiency is a necessity nowadays, with a view to reducing CO2 emissions. The application of new magnetic materials is one method for increasing the efficiency of motors, although prediction and calculation of the losses in the magnetic circuits of motors are also very important. To reduce the iron losses, iron powder composites are used here instead of Fe-Si laminated steel sheets, in a low-power switched reluctance motor (SRM), and a new method of determining the iron loss in the magnetic circuit is developed. Through the use of new materials in the motor, we can achieve a 19% decrease in the iron loss in comparison to a commercial motor. A comparison of the measurement results for our new method of iron loss prediction showed that the difference is approximately 20-26%, depending on the type of magnetic circuit used for the motor. The usage of soft magnetic composites rather than electrical steel sheets can decrease the loss in magnetic circuits. The results from our new method of determination of iron loss are in good agreement with results of measurements, meaning that it can be applied in a detailed analysis of SRMs. The application of soft magnetic composites in electromagnetic and electromechanical converters is a new method for lowering iron losses in new devices. The determination of such losses in SRMs is still challenging, and the proposed method broadens the state of the art in this area. The method developed here is based on motor current measurements, FEM calculations, and measurements of the magnetisation curves and iron loss for samples according to IEC standards. The results obtained in this work can be used by designers for the development of new kinds of SRMs.

Modelling of unit differential reversal curves in the G2E hysteresis model

Iron losses represent a crucial factor in the design process of electrical machines. For this reason, it is important to consider adequate hysteresis behavior within models which predict iron losses. A promising hysteresis description is the static hysteresis model developed by the G2Elab research group. It proposes a unique approach of modelling magnetization curves inside the major loop based on the so-called unit differential reversal curves (uDRCs). This work focuses on modelling of the uDRCs. We analyzed the accuracy and identification approaches of several uDRC models, where we compared the established model with simplified solutions with less parameters. Further, we analyzed the impact of individual models regarding identification convergence. Finally, we highlighted which simplified models give the best trade-off between the ease and stability of identification process of parameters, and the accuracy of the hysteresis model.

Iron Loss Prediction By Using Conditional Generative Adversarial Network for High-Frequency Magnetic Components

Iron Loss Prediction By Using Conditional Generative Adversarial Network for High-Frequency Magnetic Components

Improved Iron Loss Prediction Models for Interior PMSMs Considering Coupling Effects of Multiphysics Factors

This article presents improved iron loss analytical prediction models for interior permanent magnet synchronous motors (IPMSMs) used in electric vehicles. The effects of slotting harmonics, pulsewidth modulation (PWM) carrier harmonics, temperature rise, and mechanical stress are considered in the proposed models. Specifically, by investigating the stator flux density as piecewise linear with trapezoidal waveform, the iron losses in the teeth and yoke regions are calculated separately, considering the different magnetic field distributions and waveforms. To deliberate the PWM harmonic influence, a correction coefficient is added to the hysteresis loss models, while the eddy current loss models are updated by summing all the eddy current losses caused by the power supplying current harmonics. Moreover, the coupling interaction effects of magnetic, thermal, and stress fields on the empirical coefficients of hysteresis and eddy current losses are analyzed in detail and also implemented in the iron loss prediction process. The feasibility and superiority of the proposed models are verified by numerical and experimental case studies on an IPMSM prototype.

New formulation of Loss-Surface Model for accurate iron loss modeling at extreme flux density and flux variation: Experimental analysis and test on a high-speed PMSM

The Loss-Surface (LS) model, commonly used for the prediction of iron losses in electrical machines, is reformulated for better accuracy and reliability when it is extrapolated beyond the parametric identification region. First, the dynamic hysteresis behaviors of typical soft magnetic materials are analyzed by both measurements and FE simulations. They are then described by the LS model using different mathematical formulations. The most relevant one, which requires 14 parameters to reproduce and extrapolate any loss surface, will be presented in this paper. The newly reformulated LS model is tested, in the last part, to estimate the iron losses of intrinsic measurements as well as of an electrical machine. In the first case, the results obtained of four different materials are generally within 5% error. And in the latter case of a high speed permanent magnet synchronous motor, by performing a study on the effects of manufacturing methods (punching and assemble) with respect to the iron losses, it is shown that the LS model provides an estimation with around 10% of deviation.

Analytical Iron Loss Evaluation in the Stator Yoke of Slotless Surface-Mounted PM Machines

One of the attractive benefits of slotless machines is low losses at high speeds, which could be emphasized by a careful stator core loss assessment, potentially available already at the predesign stage. Unfortunately, mainstream iron loss estimation methods are typically implemented in the finite element analysis environment with a constant coefficients dummy model, leading to weak extrapolations with huge errors. In this article, an analytical method for the iron loss prediction in the stator core of slotless PM machines is derived. It is based on the extension of the 2-D field solution over the entire machine geometry. Then, the analytical solution is combined with variable coefficient loss models (VARCO) or constant coefficient loss models, which can be efficiently computed by vectorized postprocessing. VARCO loss models are shown to be preferred at a general level. Moreover, this article proposes a lookup-table-based solution as an alternative approach. The main contribution lies in the numerical link between the analytical field solution and the iron loss estimate, with the aid of a code implementation of the proposed methodology. First, the models are compared against a sufficiently dense dataset available from laminations manufacturer for validation purposes. Then, all the methods are compared for the slotless machine case. Finally, the models are applied to a real case study and validated experimentally.

Empirical Implementation of the Steinmetz Equation to Compute Eddy Current Loss in Soft Magnetic Composite Components

Finite element analysis of magnetic materials allows accurate prediction of losses and is crucial in the design of electromagnetic devices and products. Soft magnetic composites are an alternative to silicon steel laminations, yet the electromagnetic material properties are less well documented and include uncertainties which can lead to inaccurate iron and Joule loss computations. The microstructure of soft magnetic composites, which is based on ferromagnetic particles coated by inorganic resistive insulation, makes the process of iron loss prediction unique. Composite core materials require further attention by design engineers in terms of the effect of component size and pressing processes on core loss predictions, which for laminations uses the well-known Steinmetz law. This study accesses the existing soft magnetic composite core loss modelling trends using experimentally measured results. The challenges of estimating and using Steinmetz core loss coefficients via curve fitting approaches are discussed. The study indicates that soft magnetic composite components need to be treated differently to laminated iron cores. Modelling the composite materials in finite element software requires experimentally informed loss models to be able to accurately compute power losses under varying magnetic flux density and electrical frequency. An approach is suggested which can predict iron losses to within 7%, but is only validated for component cross sectional areas of 144 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> or less.

Experimental Determination of Equivalent Iron Loss Resistance for Prediction of Iron Losses in a Switched Reluctance Machine

Switched reluctance machine (SRM) has progressed as a suitable contender in the family of electrical machines for its high starting torque and better performance in different operating conditions. The need for accurate modeling of this machine is, therefore, dependent on the accurate determination of losses of which iron loss is exigent. This article aims to determine iron loss from the equivalent circuit parameters of the machine based on the input data tables that are directly obtained from the experiments carried out on an existing four-phase, bifilar wound SRM. The procedure and necessary steps for the loss separation method are well defined and supported by the theoretical and practical results. The experimental determination of the iron losses has been compared with the predicted results under the same operating conditions.

Investigation and Prediction of High-Frequency Iron Loss in Lamination Steels Driven by Voltage-Source Inverters Using Wide-Bandgap Switches

The availability of wide-bandgap (WBG) power switches makes it possible to excite electric machines with much higher pulsewidth modulation (PWM) switching frequencies. This article experimentally characterizes the magnetic and iron loss properties of lamination steels over a wide range of PWM frequencies and magnetization states excited by a voltage-source inverter using GaN-based WBG switches. With the collected hysteresis loop and iron loss data, a new frequency-interconnected dynamic Jiles-Atherton (J-A) model has been developed explicitly for PWM-induced iron loss analysis. Experiments have been conducted to verify the model prediction accuracy over a broad range of operating conditions. The proposed model has been subsequently integrated with a dynamic hysteresis model devoted to fundamental field iron loss estimation, providing an accurate and efficient tool for total iron loss estimation during PWM voltage excitation using high-frequency WBG devices. This article includes an investigation of the impact of elevated fundamental frequencies and field amplitudes on the total iron loss including comparisons between analytical predictions and experimental test results.

Modified loss separation in FeSi laminations under arbitrary distorted flux

Accurate iron loss prediction of the laminated transformer cores becomes vital for efficiency improvement and thermal optimization. In this paper, an investigation of the frequency dependence of the excess loss is carried out. The excess loss, which is mostly influenced by the domain motion and relaxation of dynamic magnetizing, is proposed to obey a new nonlinear function of the magnetizing frequency. Two excess loss factors are defined to describe the effect of frequency on dynamic excess magnetizing. A modified piecewise hysteresis loss model is proposed to represent the influence of domain rotation under a strong applied field. Then, a modified loss separation model is proposed under the sinusoidal flux. Furthermore, a correction factor of hysteresis loss is introduced to improve the modified model to predict the harmonic loss, considering the influence of the distorted flux on the hysteresis loss. In this case, the total energy loss can be modeled with a sufficient accuracy even under conditions that the statistical theory of loss is not applicable.

Iron loss prediction in high‐speed permanent‐magnet machines using loss model with variable coefficients

Iron loss prediction is extremely crucial to performance and thermal analysis for high-speed permanent-magnet (PM) machines, but it is difficult to conduct at arbitrary frequency especially high frequency. Due to the frequency-dependent coefficients of iron loss model, although iron loss can be calculated by loss model with constant coefficients under fixed frequencies, it is not feasible to measure the required loss data of non-oriented electrical steel under each frequency. Moreover, toroid tester is better to obtain accurate loss data of steel, but the data under specific high frequency cannot be directly measured due to the limitation of equipment and tester. Thus, this study presents a three-term iron loss model with variable coefficients to predict iron loss of machines at arbitrary frequency, especially high frequency based on measured loss data of steel at low frequency. Although both proposed three-term model and conventional two-term model with variable coefficients can predict iron loss of machines at an arbitrary frequency, the comparison shows that the accuracy of the three-term model is higher. In addition, experiments validate that proposed three-term model with variable coefficients is accurate and feasible for iron loss prediction in high-speed PM machines.

Advanced approach for static part of loss-surface iron loss model

Hysteresis models allow the prediction of iron losses in materials under complex magnetic excitation, with accuracy depending on their principle and identification procedure. Commonly, to achieve high accuracy, a model may require a broad set of experimental input data, which is in some cases, not easy to obtain. We propose, in this study, a static model focusing on simplicity while still striving for accuracy. Its principle is to represent the variation of the field deviation between reversal curves and a near saturation hysteresis loop. In terms of input data, this model requires the experimental first magnetization curve and a few quasi-static centered hysteresis loops of the material. Following the principle description, the identification procedure, the model validation, as well as a sensitivity study are presented in detail.

Improved Evaluation of Magnetic Loss Inside Silicon Steel Laminations Under 3-D Multi-Harmonic Magnetizations

An improved method is presented to study the efficient numerical modeling of stray-field loss in magnetic shielding excited by a 3-D harmonic magnetic field. The experimental platform is established to obtain the magnetic properties and loss model of the silicon steel lamination under harmonic magnetizations. Experimental determination is improved for more accurate stray-field loss based on the engineering-oriented model TEAM Problem 21c-M1. Prediction and evaluation of stray-field loss and additional iron loss due to harmonic excitations are achieved by special treatment of material properties in the numerical calculation of the harmonic magnetic field.

Fast Iron Loss and Thermal Prediction Method for Power Density and Efficiency Improvement in Switched Reluctance Machines

To effectively find an optimal solution in the development process of electrical machines, it is essential to predict machine iron loss and thermal behavior in the predesign stage. Most existing publications only consider thermal behavior by roughly limiting the maximum current density. However, especially for high-speed application, iron loss can also be an essential part in thermal-behavior estimation. This paper proposes a simplified model to efficiently calculate iron loss and thermal behavior in the design procedure of switched reluctance machines. For this purpose, a maximum coenergy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. Besides, a simplified lumped parameter thermal network is applied for machine sizing. Furthermore, the impact of number of turns and peak magnetomotive force on the losses and machine sizing is discussed. The efficiency and power density of various machine geometries are comparatively analyzed. Finally, the accuracy of the simplified model is verified by comparison with finite element simulations and experiment results.

Fast iron loss prediction method in the pre‐design stage of SRMs

To effectively find a valid solution in the development process of electrical machines, it is essential to predict machine iron loss in the pre-design stage. In guidance on the choice of the most suitable configuration of the switched reluctance machine (SRM) is given, but iron loss prediction is neglected. Based on the study of Burkhart et al., this study proposes a simplified model to efficiently calculate iron loss in the design procedure of SRMs. For this purpose, a maximum co-energy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. The accuracy of the simplified model is verified by comparison with finite element simulation results. Furthermore, the impact of different parameters on the iron loss is discussed, and the iron loss of various machine geometries is comparatively analysed.

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%.

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.

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.

Advanced Soft- and Hard-Magnetic Material Models for the Numerical Simulation of Electrical Machines

Accurate modeling of soft- and hard-magnetic materials for the numerical simulation of rotating electrical machines is required to allow predictions on the operational characteristics along the torque-speed map, already in the design stage. The full potential of most appropriate material selection and concurrent geometry adaption can only be utilized if models can represent actual material behavior. The accurate prediction of iron losses of soft-magnetic materials for various frequencies and magnetic flux densities, as well as the degradation due to manufacturing is eminent for the design of electrical machines. Therefore, advanced material models need to be adapted and their accuracy examined to further improve the modeling and enable progression. This paper will give an overview of the current modeling approaches applied at the Institute of Electrical Machines for soft- and hard-magnetic materials in the simulation of rotating electrical machines. A case example in the form of a traction drive is presented to which the models are applied. For the machine modeling, the inhouse solver pyMOOSE is utilized. In order to determine the losses with regard to manufacturing processes, the iron-loss model with material degradation is used in combination with a machine simulation scheme of the entire operating range of the machine. Here, various simulation approaches are combined to form the entire computational toolchain to obtain accurate results in the entire operational range.

A Fast Identification Method for Rotor Flux Density Harmonics and Resulting Rotor Iron Losses of Inverter-Fed Induction Motors

Inverter-fed induction machines’ iron loss characteristics become more complicated because of the harmonics brought by pulse width modulated (PWM) voltage-source power supplies. There are two main difficulties associated with analyzing the iron loss of induction motors, i.e., the identification of rotor flux density harmonics and the accurate prediction of iron loss. In order to reveal the iron loss characteristics of the inverter-fed induction motors, especially on the rotor side, a computationally efficient rotor flux density harmonics identification method using data from a single power supply fundamental frequency cycle (supply cycle) is proposed. With the proposed identification method, the rotor iron loss characteristics of a 5.5- and a 30-kW inverter-fed induction motors are investigated. By comparing the predicted and measured iron losses of those induction motors under both sinusoidal and PWM inverter voltage supplies, the effectiveness of the proposed method is validated. Furthermore, the detailed iron loss characteristics of the two induction machines under both sinusoidal and PWM inverter voltage supplies are studied.