Iron Loss Calculation Research Articles

Adaptive Current Angle Compensation Control Based on the Difference in Inductance for the Interior PMSM of Vehicles

Achieving good performance in terms of a fast and accurate maximum torque per ampere (MTPA) control method depends on both accurate parameter estimation and a sophisticated control strategy. However, it often requires a complex and long computational process. This paper proposes an efficient control method using the relationship of torque and current angle for maximum torque per ampere control of the interior permanent magnet synchronous motor (IPMSM) for vehicle application. It was found that it is not necessary for the control method to determine the inductance in the d-q axes; the identification of the difference between each axis is enough. Furthermore, this paper presents a simple and effective procedure to estimate the difference in inductance online, where the linear iron loss calculation method is also designed to support the above process. The proposed control method was experimentally validated on a 5 kW prototype by a TMS320F28335 microcontroller and DSPACE synchronized with a personal computer. The results show that the control process has faster and more accurate performance than the conventional method.

A Dynamic Hysteresis-Based Iron Loss Calculation Method and Its Application in a PWM-Fed IPMSM

A Dynamic Hysteresis-Based Iron Loss Calculation Method and Its Application in a PWM-Fed IPMSM

Iron loss calculation model of high‐voltage multi‐pole asynchronous motors considering high harmonic flux density

AbstractA variable coefficient segmented iron loss calculation model is proposed for high‐voltage multi‐pole asynchronous motors, which fully considers the influence of high‐order harmonic magnetic density on such motors. This model introduces additional hysteresis loss, improved eddy current loss coefficients, and rotation magnetisation coefficients to account for changes in losses due to small hysteresis loops and skin effect, as well as rotation magnetisation losses. A 710 kW 10‐pole asynchronous motor is used as the research object. A full‐domain iron loss calculation model considering stator and rotor tooth top surface losses is developed. The model can be used to accurately calculate the overall and local harmonic iron losses in the stator and rotor cores of high‐voltage multi‐pole asynchronous motors. And quantitatively analyse the distribution pattern of any harmonic iron loss at any location in the motor core. It realises the refinement calculation and analysis of iron losses in the whole domain of high‐voltage multi‐pole asynchronous motor. Finally, no‐load and load tests were conducted on the 710 kW 10‐pole asynchronous motor. The iron losses of the 710 kW 10‐pole asynchronous motor at no load and load are calculated using the above mentioned model and the classical trinomial constant factor model. The results are compared with the measured iron loss values. It is proved that the iron loss model is more accurate in calculating the iron loss of high‐voltage multi‐pole asynchronous motor. The result helps researchers to calculate the iron loss distribution of high voltage motors more accurately. Thus, the design and operating parameters of the motor can be optimised to improve the efficiency and performance of the motor. It provides the necessary technical support and key basis for the reduction of loss and energy saving of high‐voltage motors and the optimisation of their core structure.

Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.

Two-Dimensional FEA-Based Iron Loss Calculation Method for Linear Oscillating Actuator Considering the Circumferential Segmented Structure

Linear compressors have a simpler mechanical structure and higher efficiency than the rotary compressors because they have lower frictional loss. Therefore, the linear oscillating actuator (LOA) is an attractive option for compressors owing to its high power density and efficiency. However, the complex structure of LOA such as segmented outer stator and mover leads to conduct 3-D finite element analysis (FEA) for calculating accurate iron loss, but it requires high computation cost. Thus, we proposes a method to calculate iron loss only using 2-D axisymmetric FEA considering the permeance in stator core. To compensate for the alterations in the magnetic flux density resulting from the structure of the mover and outer stator of the LOA, two equivalent coefficients are applied in permanent magnets in mover to correct the induced voltage and outer stator to correct the magnetic flux density. Through the utilization of these two equalization methods, iron loss can be accurately calculated using only 2-D axisymmetric FEA. The proposed method can be used to accurately determine the efficiency of the LOA without 3-D FEA, thus, making the LOA design process more efficient.

Iron Loss Calculation Methods for Numerical Analysis of 3D-Printed Rotating Machines: A Review

Three-dimensional printing is a promising technology that offers increased freedom to create topologically optimised electrical machine designs with a much smaller layer thickness achievable with the current, laminated steel-sheet-based technology. These composite materials have promising magnetic behaviour, which can be competitive with the current magnetic materials. Accurately calculating the iron losses is challenging due to magnetic steels’ highly nonlinear hysteretic behaviour. Many numerical methodologies have been developed and applied in FEM-based simulations from the first introduced Steinmetz formulae. However, these old curve-fitting-based iron loss models are still actively used in modern finite-element solvers due to their simplicity and high computational demand for more-accurate mathematical methods, such as Preisach- or Jiles–Atherton-model-based calculations. In the case of 3D-printed electrical machines, where the printed material can have a strongly anisotropic behaviour and it is hard to define a standardised measurement, the applicability of the curve-fitting-based iron loss methodologies is limited. The following paper proposes an overview of the current problems and solutions for iron loss calculation and measurement methodologies and discusses their applicability in designing and optimising 3D-printed electrical machines.

Dynamic Hysteresis Calculation of Silicon Steel Considering DC Hysteresis and Anomalous Eddy Current Loss

We had proposed a combined 3D solid core and 1D steel plate modeling method for accurate magnetic field and iron loss calculation of laminated iron cores with low computation cost. The effective permeability including the laminated effect can be obtained and fed back to the 3D model. However, only the initial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BH</i> curve and classic eddy currents are considered in the 1D model, thus it is improved to consider the DC hysteresis property and anomalous eddy current loss in this paper. The improved 1D model is applied to the dynamic hysteresis loop calculation of non-oriented and grain-oriented silicon steels and confirmed to be effective by comparing with the measurement results.

Estimation of DC Hysteresis Property Using Nonlinear Eddy Current Analysis Considering Hysteretic Property

The DC hysteresis property should be considered in the nonlinear eddy current analysis for accurate hysteresis loop and iron loss calculation of iron cores composed of silicon steels. However, the DC hysteresis property is usually measured at ultra-low frequency (e.g., 0.01 Hz) and it is quite difficult due to very low signal to noise ratio. In this paper, we proposed a method to derive the DC hysteresis property from the AC hysteresis property measured at 20 Hz, which is relatively easy to be measured. The eddy current effect in the 20 Hz hysteresis loop is removed by using an iterative eddy current analysis considering hysteretic property. The estimated DC hysteresis property obtained from the proposed method are compared with those obtained from a conventional estimation method. Finally, the obtained DC hysteresis property are used in the AC hysteresis loops and iron losses calculation (50∼1 kHz) of a non-oriented silicon steel and compared with the measured ones. The hysteresis loops and iron losses can be well represented.

Reluctance Network Model of IPM Motor Representing Dynamic Hysteresis Characteristics for High-Accuracy Iron Loss Calculation Considering Carrier Harmonics

It is essential to establish a simple and practical method for quantitatively estimating the iron loss considering the dynamic hysteresis behavior to further improve the efficiency of electric machines. In a previous study, a novel simple magnetic circuit model representing the dynamic hysteresis characteristics was presented by incorporating a play model, one of the phenomenological dc hysteresis models, and a Cauer circuit, which can consider the skin effect. It was demonstrated that this magnetic circuit model could accurately calculate the hysteresis loops and iron loss even under PMW excitation for magnetic reactors made of several types of core materials in a short time. However, this method can only be used for an object with a simple shape, such as a ring core. Hence, this paper describes that the previously proposed magnetic circuit model is extended to a reluctance network analysis (RNA) to expand the application range. Furthermore, the proposed method was experimentally validated using an interior permanent magnet (IPM) synchronous motor driven by a PWM converter as the examination target.

Iron loss evaluation method for SiFe sheet material under PWM inverter excitation

This study proposed an iron loss evaluation method for SiFe sheet material under high-frequency current ripple caused by pulse width modulation (PWM) inverter switching. The PWM inverter switching results in dynamic minor loops on the BH plane, leading to iron loss. Furthermore, in magnetic material with wide major loops, such as SiFe sheet material, the iron loss caused by one dynamic minor loop differs based on the flux density bias difference even for the same magnetic field bias. A previous study reported an iron loss calculation error because the conventional iron loss calculation method neglected the iron loss of the flux density bias characteristic in the same magnetic field bias. Therefore, we modified the previously developed iron loss measurement system to collect the iron loss of the flux density bias characteristic and proposed an iron loss calculation method for SiFe sheet material.

Study on magnetic properties of ferromagnetic materials in cryogenic conditions

Cryogenic motors are the main actuators in submerged pumps used in liquefied natural gas (LNG) transportation industry. When cryogenic motors are submerged and operating in an LNG environment with a temperature lower than 110 K, the motor performances will be affected by changes in magnetic conductivity and loss properties of ferromagnetic materials used. However, there still exist obvious confusions in iron loss calculations of cryogenic motors in existing studies. Some studies claimed that the calculated iron loss increased greatly at cryogenic temperatures, whereas some other studies insisted that iron loss would maintain unchanged from room temperatures to cryogenic ones. In most studies, loss characteristics were studied, while the magnetic conducting performances were neglected. In this paper, based on summarizing related prior studies, cryogenic characteristic measurements were designed and conducted aiming at three types of silicon steel sheets materials that can be used in cryogenic motors, including 35W300, 50W600, and 2AT1500, according to Chinese national standard GB/T 2521-2008. Tested data and errors were compared and analyzed. According to the results, it can be concluded that the general losses increase an average of 20 % at cryogenic temperatures, which corresponding to the 20–30 % increase of electrical conductivity values ranges at cryogenic temperatures. Besides, the magnetic performances at cryogenic temperatures are enhanced, including the values of saturation magnetic density, remanence, and coercivity, etc.

Iron Loss Calculation for FSPM Machine With the PWM Inverter Supply Based on General Airgap Field Modulation Theory

In this article, an iron loss for the flux switching permanent magnet machine with the pulsewidth modulation (PWM) inverter supply is calculated based on the general airgap field modulation theory (GAFMT). First, the amplitudes and rotation speeds of the main space harmonics of the airgap magnetic flux density with the supply of PWM harmonics are calculated based on the GAFMT. When calculating the source magnetizing magnetomotive force based on the GAFMT, the equivalent magnetic circuit is applied to calculate the working point of the permanent magnet. Second, each spatial harmonic in the airgap magnetic field is synthesized by an equivalent current layer in the airgap and then is substituted into the 2-D finite element analysis models to calculate the iron losses. Finally, the iron losses calculated by the proposed method with different speeds, loads, and control strategies are compared with the experimental results.

Improvement of core loss model under AC–DC hybrid excitation and verification based on TEAM P21 extended model

HVDC (High Voltage Direct Current) transmission equipment generally works under AC–DC hybrid excitation (with both DC-bias components and harmonics), increasing the loss of magnetic components and even causing local overheating. In order to study the influence of AC–DC hybrid excitation on the performance of magnetic materials, the loss properties of electrical silicon steel under AC–DC hybrid excitation are measured and modelled. This paper improves the iron loss model based on the Bertotti loss model by introducing a new term caused by DC bias. In addition, the improved loss model is combined with the transient loss model, and the iron loss calculation is performed based on the results of the TEAM P21 extended model. The comparison between the calculated and measured loss shows that the improved loss model is more accurate than the traditional one.

Calculation of Iron Loss with Stress in Stator Core by Shrinkage Tolerance

This paper presents a method for calculating the iron loss of the stator core of an electric motor owing to the stress generated by shrink fit tolerance in the production process. Shrink fit is used to fix the frame and stator core, shaft, and rotor core in a motor, and the corresponding condition varies depending on the material. Three finite element analysis steps were performed to reduce the iron loss owing to stress during shrinking of a frame and stator core. In step 1, the shrink fit process was applied to a frame and stator core, considering the manufacturing tolerances through three-dimensional modeling. Thermal-structure finite element analysis was performed to apply the same conditions as those in the shrink fit process. The shrink fit was expanded by applying heat to the frame, followed by natural cooling to calculate the contact stress between the frame and the stator core. In step 2, the same contact stress as that in step 1 was derived using structural analysis through two-dimensional modeling of the frame and stator core without tolerances. The contact stress was calculated by applying the equivalent thermal expansion coefficient of the frame, and it was confirmed that the manufacturing tolerance and maximum stress intensity are linearly related. In step 3, electromagnetic analysis was performed at the rated operating point of the 2.2-kW induction motor using the model obtained in step 2. The magnetic flux density distribution of the stator core was derived via electromagnetic analysis and the iron loss, including the stress distribution, realized in step 2. The iron losses obtained under different conditions, including the stress of the stator core owing to the shrink fit tolerance, were compared, and the effectiveness of the shrink fit tolerance required to achieve a motor with high efficiency was evaluated.

Accurate Calculation of Iron Loss of High-Temperature and High-Speed Permanent Magnet Synchronous Generator under the Conditions of SVPWM Modulation

The high-temperature and high-speed permanent magnet synchronous generator (HTHSPMSG) is the core component ensuring the efficient and safe operation of the high-speed aircraft power supply system. At present, the existing iron loss model fails to meet the requirements for the precise calculation of the iron loss of HTHSPMSG under high-temperature and high-frequency conditions. In this paper, a 40 kW, 18,000 rpm HTHSPMSG is used to study the accurate calculation of iron loss at an ambient temperature of 350 °C. Considering the influence of high temperature and high frequency on the loss and performance of electromagnetic materials, a test platform for the loss performance of the magnetic core materials is established. Then, according to the loss performance of the electromagnetic material, the corresponding iron loss coefficient is fitted by the variable coefficient iron loss separation model. In addition, the digital twin field-circuit co-simulation method is proposed to guarantee the accuracy of the iron loss calculation. Then, the influence of carrier frequencies and modulation ratios on the iron loss characteristics of the HTHSPMSG under the conditions of SVPWM modulation is studied. Lastly, the effectiveness of the proposed method is verified by the experimental results, which provide a reference for the accurate analysis of iron loss of the same type of HTHSPMSG.

Iron Loss Analysis in Axial Flux Permanent Magnet Synchronous Motors With Soft Magnetic Composite Core Material

Accurate calculation of iron losses in electric machines are essential for optimal machine design. This is especially important for axial flux permanent magnet motors with solid stator and rotor cores. This paper investigates iron losses in axial flux motor with soft magnetic composite material for the stator and the rotor. First, an analytical model is proposed to estimate iron losses under different operating conditions. The model is based on combining the Quasi 3-D computation approach and the magnetic equivalent circuit. The proposed model can be modified to account for different motor topologies and different stator and magnet designs. Then, The effect of torque ripple, on iron losses, is studied by comparing two motors: an optimized motor, with dummy pocket added to the stator tooth, and a non-optimized motor. Finally, the effect of the motor control scheme and the inverter switching frequency is studied by implementing two controllers: the Field Oriented Control and the Trapezoidal control. Simulations using 3-D finite element analysis and experimental tests are conducted, under different operating conditions, on two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{12}\,V$</tex-math></inline-formula> , 6 slots, 8 poles single stator single rotor fractional horsepower axial flux permanent magnet synchronous motor prototypes. Test results shows that motor optimization, the choice of the motor control scheme, and the selected switching frequency can significantly affect iron losses in the motor. Using an optimized motor with a smooth control scheme, such as Field Oriented Control, and a high switching frequency, can cause a variation in iron losses by around 14% compared to a non optimized motor with the Trapezoidal control and a low switching frequency.

An adaptive method for calculation of iron losses in switched reluctance motors using a minimum number of magnetostatic finite element simulations

PurposeThis paper aims to present an adaptive method based on finite element analysis to calculate iron losses in switched reluctance motors (SRMs). Calculation of iron losses by analytical formulas has limited accuracy. On the other hand, its estimation in rotating electrical machines through fully dynamic simulations with a fine time-step is time-consuming. However, in the initial design process, a quick and sufficiently accurate method, i.e. a value close to that of iron losses, is always welcome. The method presented in this paper is a semi-analytical approach. The main problem is that iron losses depend on d B/d t. Therefore, the accuracy of the calculation of iron losses depends on the accuracy of the calculation of the first derivative of the flux density waveform. When adopting a magnetostatic model to estimate the iron losses, an important question arises: by how many magnetostatic simulations can the iron losses be estimated within the desired accuracy? In the proposed algorithm, the aim is not to accurately calculate the value of iron losses in SRMs. The objective is to find a numerical error criterion to calculate iron losses in SRMs with a minimum number of magnetostatic simulations.Design/methodology/approachA finite element solver is developed by authors in MATLAB to solve the 2 D nonlinear magnetostatic problem using the Newton–Raphson method. A parametric program is developed to create geometry and mesh. The proposed method is implemented in MATLAB using the developed solver. Counterpart simulations are done in the ANSYS Maxwell software to validate the accuracy of the results generated by the developed solver.FindingsThe performance of the proposed method is studied on a 12/8 (500 W) SRM. Three scenarios are studied. The first one is the calculation of iron losses by uniform refinement, and the second one is by adaptive refinement, and the last one is by adaptive refinement started by particular initial points (switching points). According to the results, the proposed method substantially reduces the number of magnetostatic simulations without sacrificing accuracy.Originality/valueThe main novelty of this paper is introducing an error criterion to find the minimum number of magnetostatic simulations that are needed to calculate iron losses with the desired accuracy.

Iron Loss Analysis and Efficiency Calculation of Double Stator HTS Machine With Stationary Seal

This paper mainly analyzes the iron loss of a double stator high-temperature superconducting (HTS) machine with stationary seal and calculates its efficiency. Firstly, according to the field-modulation theory, the air-gap flux density harmonic components of the excitation magnetic field and armature-reaction magnetic field are derived. Secondly, the air-gap flux density harmonic components corresponding to the flux density harmonic components of iron core are derived, and the main iron core flux density harmonic components are confirmed and verified by finite element analysis (FEA). When using the semi-FEA method to calculate the iron loss, according to the analysis results of main core flux density harmonic components, the appropriate step-interval of the FEA is selected. In addition, according to the characteristics of the iron core flux density, this paper proposes an alternative method of iron core flux density, which greatly shortens the calculation time of semi-FEA method of iron loss calculation. Then, iron loss model with variable coefficients is used to calculate the iron loss of the machine, and the calculation results are compared with those of the FEA iron loss calculation method. Finally, other losses and efficiency of the machine are calculated.

Iron loss estimation of soft magnetic materials for power transformers considering hysteretic properties

AbstractThis paper investigates an efficient iron loss calculation method for soft magnetic materials of power transformers. To accurately estimate iron losses in the materials such as structural steels and magnetic shielding, the magnetic field analysis taking account of the nonlinear magnetic properties is considered to be effective. In this paper, quasi‐DC magnetic properties of the soft magnetic materials are measured and the iron losses are calculated by using a three‐dimensional finite element method. Furthermore, the effect of considering hysteretic properties on the accuracy of the iron loss calculation is discussed based on a theoretical representation of eddy current loss in a conductive rectangular plate.

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

PurposeThe 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/approachThe 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.FindingsThe 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/valueBased 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.