Iron Loss Estimation Research Articles

Efficient Iron Loss Estimation of Interior PMSMs in Electric Vehicles: Analytical Modelling and Experimental Validation

Efficient Iron Loss Estimation of Interior PMSMs in Electric Vehicles: Analytical Modelling and Experimental Validation

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.

Computationally Efficient Estimation of PWM-Induced Iron Loss of PMSM Using Deep Transfer Learning

Computationally Efficient Estimation of PWM-Induced Iron Loss of PMSM Using Deep Transfer Learning

Estimation of iron losses in Terfenol-D for variable DC-biased and AC excitation conditions

Giant magnetostrictive material (GMM) is well known for its hysteresis behaviors related to multiple influencing factors, such as bias magnetic field and excitation level. This feature greatly challenges the loss prediction of giant magnetostrictive transducers. The existing loss prediction models need further improvement to cover the sensitive magnetic properties of GMM. In this paper, the variations of the iron losses with the operation conditions are interpreted by the variations of the Jiles–Atherton model parameters, and a concise parameter identification procedure with great generalizability is proposed. The iron losses of Terfenol-D under variable excitation levels and the DC biases are further investigated. With the proposed procedure, the introduced eight additional modification parameters for the original Jiles–Atherton model are easy to obtain, and merely a few hysteresis loops of Terfenol-D are involved. An experimental setup is established for further demonstration. Comparisons between the measured and calculated results indicate the effectiveness of the proposed approach. Throughout the bias-excitation space within the frequency range under investigation, the iron losses can be precisely estimated with an acceptable error of less than 5%.

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.

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.

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.

Sliding Mean Value Subtraction-Based DC Drift Correction of B-H Curve for 3D-Printed Magnetic Materials

This paper presents an algorithm to remove the DC drift from the B-H curve of an additively manufactured soft ferromagnetic material. The removal of DC drift from the magnetization curve is crucial for the accurate estimation of iron losses. The algorithm is based on the sliding mean value subtraction from each cycle of calculated magnetic flux density (B) signal. The sliding mean values (SMVs) are calculated using the convolution theorem, where a DC kernel with a length equal to the size of one cycle is convolved with B to recover the drifting signal. The results are based on the toroid measurements made by selective laser melting (SLM)-based 3D printing mechanism. The measurements taken at different flux density values show the effectiveness of the method.

Iron Loss Modeling in Dual Stator Winding Induction Machines With Unequal Pole Pairs and Squirrel Cage Rotor

This article pertains to dual stator winding induction machines (DSWIMs) modeling and proposes an equivalent electrical circuit for DSWIMs that considers the iron loss effect. It is proved that the iron loss in DSWIMs is comprised of two independent parts resulting from their winding sets. Accordingly, the proposed model is comprised of two distinct parts and it is based on the assumption of independent operation of DSWIM winding sets. This model is valid in dynamic and steady-state operation conditions. The dq0 form of the proposed model is derived in an arbitrary reference frame. In addition, based on this model the iron loss estimation in DSWIMs is presented. The proposed model is simulated in MATLAB/Simulink based on the parameters obtained from IEEE standard characterization tests performed on a 3.3-kW DSWIM and also some experimental tests are executed via a digital signal processor (DSP)-based DSWIM drive system. Comparison of the simulation and experimental results confirms the validity of the proposed model both in steady-state and transient conditions. Moreover, this comparison clarifies that the proposed iron loss estimation method can account for the iron loss with high accuracy.

A New Flux Linkage Estimation with Drift Cancellation Technique for Switched Reluctance Machines

Assessing the flux linkage is an important feature for electric machines, including switched reluctance machines. However, sensor calibration errors and noise impose a problem known as drift, which is caused by the integration process used to estimate the flux linkage. This paper proposes a flux linkage estimation with a drift cancellation technique. The technique accounts for the switched reluctance machine’s working and driving principles. Simulation and experimental results are used to validate the proposed method. Additionally, a goodness-of-fit analysis is performed to support the outcomes. A R 2 factor of 0.9823 is found between the experimentally and simulation-estimated flux linkage. The results indicate that the method works properly and may be further used in control techniques and iron losses estimation.

Estimation of PWM-Induced Iron Loss in IPM Machines Incorporating the Impact of Flux Ripple Waveshape and Nonlinear Magnetic Characteristics

This article presents an improved estimation model to predict the additional iron loss due to pulsewidth modulation (PWM) switching in interior permanent magnet (IPM) machines. The input current including the predicted PWM-induced current ripple is fed to two-dimensional (2-D) finite-element analysis to calculate the PWM-induced flux ripple distribution in the analyzed machine. A special frequency decomposition method is proposed to separate the predicted flux ripple into 1x and 2x PWM frequency components. Combining these results with the modified dynamic Jiles-Atherton model, the PWM-induced iron loss is calculated incorporating the impact of the actual flux ripple waveshape and the lamination steel's nonlinear magnetic characteristics. Experiments have been conducted to evaluate the prediction accuracy and investigate the impact of several factors on PWM-induced core losses (i.e., both iron and magnet loss) in IPM machines. These results demonstrate that the model offers promising estimation accuracy over a wide range of machine and inverter operating conditions.

Analytical approach and thermal signature of Switched reluctance motor iron losses

This paper describes a fully analytical approach for iron losses estimation. This method allows us to quantify hysteresis and Eddy current losses in each part of a Switched Reluctance Motor (SRM), the validation of this method is obtained by comparing analytical and experimental phase inductance, the other validation method is based on comparison of static flux densities between this method and finite element method. Analytical air gap permeance model is used in association with analytical expression of magneto motive force (MMF) and current. It allows calculating the flux linked in each part of the SRM. The temporal and frequency characteristics of these fluxes, using static material iron losses under sinusoidal flux density excitation, FFT and correction factors leads to estimate iron losses and their location in the machine. The results show that the major part of the losses are located in the stator especially in the teeths, Hysteresis losses are higher than Eddy current losses in the SRM. As soon as the speed of the SRM increases the Eddy current losses become more important. 8/6 2 kW SRM is used in experimental test bench, Infrared camera placed in front of the lateral face removed just after operation give the thermal signature of these losses and confirm the previous results. Copper losses are located in the winding.

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.

Iron Loss Estimation for IPM Motors based on Eddy Current Loss Correction Factor Considering Plastic Deformation by Punching

Iron Loss Estimation for IPM Motors based on Eddy Current Loss Correction Factor Considering Plastic Deformation by Punching

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.

Generalized Dynamic Hysteresis Model for Improved Iron Loss Estimation of Complex Flux Waveforms

This paper presents a generalized dynamic hysteresis model for estimating the iron loss of complex flux waveforms. A versatile measurement system is developed to experimentally investigate the magnetic and iron loss properties of the studied soft magnetic material over a wide range of ac and dc excitation. With the measured hysteresis loop data available, an improved dynamic hysteresis model is proposed to properly include the impact of the flux density changing rate, instantaneous magnetization state, and time history of magnetic flux on dynamic field modeling. Experimental results have confirmed the desired accuracy of iron loss predictions over broad operating ranges, including conditions of pure sinusoidal fields, non-sinusoidal flux waveforms with superimposed harmonics, and flux waveforms in the presence of pre-magnetized dc-bias fields. The model parameters identified from symmetrical hysteresis loops can be utilized to predict the iron loss over a variety of conditions, making it a generalized tool for the iron loss estimation of complex flux waveforms.

Iron Loss Estimation Method for Silicon Steel Sheet Taking Account of DC-Biased Conditions

One of the issues of an iron loss estimation is the modeling of an excess loss. This paper investigates the modeling method for the excess loss under dc-biased conditions. The proposed approach is based on the 1-D finite-element analysis for the silicon steel sheet in the thickness direction considering hysteretic property and skin effect. In addition, the magnetic flux density waveform is divided into the outline loop and minor loops and estimate the excess loss of each loop using the eddy current loss correction factor $\kappa $ taking account of dc-biased conditions. To demonstrate the effectiveness of the proposed method, we compare the estimated loss with measured one for the harmonic wave in single sheet tester.

Iron Loss Analysis of Interior Permanent Magnet Synchronous Motors Using Dynamic Hysteresis Model Represented by Cauer Circuit

This paper investigates the effectiveness of the Cauer circuit modeling for dynamic hysteretic property in iron loss analyses of rotating machines. We apply the method to an interior permanent magnet synchronous motors fed by pulsewidth modulation inverter and compare their performance with a conventional iron loss estimation method based on 1-D finite-element analyses. As a result, almost the same iron loss is obtained by using the Cauer circuit modeling with lower computational cost.

Fundamental Investigation of a Method for Considering Anomalous Eddy Current Loss in Post-processing for Iron Loss Estimation

Fundamental Investigation of a Method for Considering Anomalous Eddy Current Loss in Post-processing for Iron Loss Estimation

Improved estimation of iron losses for non-sinusoidal voltages

PurposeThe electrical machines connected to modern electric power grids are non-sinusoidal excited, and their augmented losses, including iron losses, limit their working characteristics. This paper aims to propose a prediction method for iron losses in non-oriented grains (NO) FeSi sheets under non-sinusoidal voltage, involving an inverse classical Preisach hysteresis model and the time-integration of each loss component.Design/methodology/approachThe magnetic history management in inverse Preisach model is optimized and a numerical Everett function is identified from measured symmetrical hysteresis cycles. The experimental data for sinusoidal waveforms obtained by a single sheet tester were also used to identify the parameters involved in Bertotti’ losses separation method. The non-sinusoidal magnetic induction waveform, corresponding to a measured voltage in an industrial electrical grid, was the input for Preisach model, the output magnetic field being accurately computed. The hysteresis, classical and excess losses are calculated by time-integration and the total losses are compared with those obtained for sinusoidal excitation.FindingsThe proposed method allows to estimate the iron losses for non-sinusoidal magnetic induction, using carefully identified parameters of FeSi NO sheets, using experimental data from sinusoidal regimes.Originality/valueThe method accuracy is assured by using a numerical Everett function, a variable Preisach grid step (adapted for the high non-linearity of FeSi sheets) and high-order fitting polynomials for the microscopic parameters involved in the excess loss estimation. The procedure allows a better design of magnetic cores and an improved estimation of the electric machine derating for non-sinusoidal voltages.