Search Results (160)

Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable importance. However, existing data-driven methods generally rely heavily on high-frequency measurements, and their prediction accuracy tends to deteriorate under low-frequency sampling conditions. Moreover, purely data-driven models lack explicit physical constraints, which limits their interpretability and generalization capability. To address these issues, this study proposes a physics-informed long short-term memory model for permanent magnet temperature prediction. A physics-based loss function is formulated using the PMSM d–q-axis voltage balance equations, while the d- and q-axis inductances are treated as trainable parameters during network optimization. This design enables the temperature prediction task and the electromagnetic constraints to be optimized jointly. Multi-operating-condition experiments are conducted using a publicly available electric motor temperature dataset, and the proposed model is compared with CNN, GRU, MLP-PINN and TNN models. In addition, experiments involving different downsampling ratios, errors in the high-temperature region, parameter sensitivity, physical parameter identification, and input-feature effects are performed to comprehensively evaluate the proposed model. The results show that the PINN-LSTM model achieves the best overall prediction performance, with an MAE of 1.6048 °C, an RMSE of 2.1890 °C, and an R² of 0.9861, outperforming all comparison models. The model also maintains high prediction accuracy in the high-temperature region, with an MAE of 1.363 °C and an RMSE of 1.896 °C. Furthermore, the parameters learned by the model can effectively reconstruct the variation trends of the d- and q-axis voltages under the test operating conditions. Sensitivity analysis of the temperature coefficients further demonstrates that the model is robust to deviations in key physical parameters. These results indicate that the proposed method can achieve accurate and robust permanent magnet temperature prediction under low-frequency sampling conditions, providing an effective solution for motor thermal-state monitoring and health management. Full article

This paper presents the integrated design and experimental verification of a ferrite spoke permanent magnet motor with rib core skew for semiconductor process pump drives. Conventional induction motors are widely used in industrial pump systems because of their robustness and cost-effectiveness; however, rotor copper loss and limited output capability under a restricted installation envelope remain practical limitations. To address these issues without rare-earth magnets, a flux-concentrating ferrite spoke rotor is applied. The proposed design procedure considers the baseline induction motor envelope, electric and magnetic loadings, manufacturable winding specifications, voltage and current density limits, irreversible demagnetization, and post-assembly magnetization feasibility. An 8-pole/12-slot topology is selected because it enables one-shot post-assembly magnetization, unlike the 10-pole/12-slot alternative requiring segmented magnetization. Rib core skew and stator tooth shoe chamfer geometries are then applied to reduce cogging torque and load torque ripple. A prototype is fabricated and tested. At 1000 rpm, the measured no-load line-to-line voltage is 21.6 V_rms. At 7000 rpm, the prototype achieves 4.028 kW output power and 93.1% efficiency. The measured post-assembly magnetization ratio is 98.7%, and the maximum winding temperature recorded during an approximately 50 min water-cooled test at 6.68 A/mm² is 57.4 °C. These results confirm the feasibility of the proposed design procedure. Full article

Magneto-active elastomers (MAEs) are polymer composites consisting of magnetic microparticles embedded in an elastomeric matrix. These materials exhibit strong magneto-mechanical coupling under external magnetic fields, resulting in tunable stiffness, reversible shape changes, and nonlinear magnetic responses. This study presents a multiscale theoretical framework to investigate the origin of magnetic hysteresis in MAEs, with emphasis on the evolution of the internal microstructure during magnetization and demagnetization. The total energy of the system is formulated as the sum of magnetic and micromechanical contributions, while macroscopic deformation of a cylindrical MAE sample is fully constrained. Particle interactions are modeled first via pure dipole–dipole interactions and then extended to include higher-order near-field effects at close particle separations. The results show that hysteresis in MAEs with magnetically soft particles primarily arises from trapped microstructural rearrangements, leading to distinct particle configurations under increasing and decreasing magnetic fields. Parametric studies demonstrate that particle volume fraction, sample aspect ratio, and matrix stiffness strongly influence the microstructure evolution and the width of resulting hysteresis loops. The proposed framework provides a solid foundation for modeling magnetic hysteresis, which is essential for the design and optimization of MAEs in practical applications. Full article

Interior permanent magnet synchronous motors have high torque density and a high salient pole effect, combine low-speed high torque with constant-power wide speed regulation, and are increasingly favored by more and more car companies and widely used in electric vehicles. With the development of interior permanent magnet synchronous motors for electric vehicle towards high speed and large capacity, the eddy current loss generated inside the permanent magnet increases rapidly when the magnetic field alternates. Simulation results show that the excessive eddy current loss can raise the permanent magnet temperature of the I2V-type rotor up to 112 °C under rated operating conditions. Such a high temperature far exceeds the stable working temperature range of conventional NdFeB materials and greatly increases the risk of irreversible demagnetization. NdFeB permanent magnet materials have high electrical conductivity but weak heat-resistant capacity, so the temperature rise of permanent magnet is more serious, and even irreversible demagnetization occurs, which is fatal for the safe operation of motors. Therefore, it is necessary to analyze and study the eddy current loss of permanent magnets, explore methods to reduce magnet loss, and design reasonable and efficient cooling systems. Firstly, this paper selects three different rotor topologies as research objects, establishes two-dimensional parameterized finite element analysis models, and analyzes and compares magnet loss and the hysteresis loss, eddy loss, and copper loss of the stator. Secondly, to solve the problem that the I2V-type rotor generates higher magnet loss than the other two structures under all working conditions, magnetic isolation holes are arranged on each rotor pole to optimize the internal magnetic circuit. Simulation analysis results show that this method can effectively reduce magnet loss and stator hysteresis losses. Finally, the temperature of the shaft, magnet and stator winding are studied; aiming at characteristics of high torque density with small size, large torque, and high magnet temperature, a cooling method combining housing cooling and shaft cooling is proposed. Simulation results indicate that the new cooling method can greatly suppress the magnet temperature rise, which reduces the maximum permanent magnet temperature from 112 °C to 80 °C under rated operating conditions and can further improve the torque density and operating reliability of interior permanent magnet synchronous motors. This provides a feasible design reference for high-reliability vehicle interior permanent magnet synchronous motors. Full article

Theswitched reluctance motor (SRM) suffers from torque ripple and speed fluctuations because of its doubly salient structure, magnetic saturation, and discrete commutation. To improve commutation performance and disturbance rejection, this paper proposes a progressive torque ripple suppression strategy. First, a multimodal magnetic co-energy model is developed to describe position-dependent saturation and generate the reference current through model inversion. Then, envelope extraction and frequency identification reveal the commutation-related periodic torque-error characteristic. Based on this feature, an angle-domain binned compensation method combining cycle averaging and linear interpolation is proposed to correct the reference current. A score-based finite-set PI hysteresis current controller is further designed to optimize magnetizing, freewheeling, and demagnetizing states, while a linear active disturbance rejection control (LADRC) speed loop improves load-disturbance rejection. Ablation studies verify the synergistic effect between angle-domain compensation and finite-set current execution. Robustness tests confirm low sensitivity to parameter variations, and theoretical analysis proves ultimate boundedness. Simulation results show that torque ripple is reduced to 2.00%, 2.03%, and 2.18% at 250, 500, and 1000 r/min, respectively. Under load-disturbance conditions, speed fluctuation is reduced by 59.92% and 57.20%, and all normalized parameter sensitivities remain below 0.35. Full article

During train operation, railway traction-returning current generates a power-frequency magnetic field around the rail, offering a potential energy source for self-powered trackside monitoring nodes. The H-shaped-core magnetic field energy harvester (MFEH) is attractive because it can be installed beneath the rail without enclosing the conductor, yet its output is strongly affected by the coupled rail-core-coil system. To clarify these effects, a three-dimensional electromagnetic-circuit-coupled finite-element model of an experimentally validated laminated-silicon-steel H-shaped-core MFEH was established to examine core and coil parameters. Increasing the center-leg and side-leg lengths weakens demagnetization but intensifies eddy-current losses, causing output power to approach saturation. Under a 50 Hz, 300 A current in a 54E1 rail and series-tuned matching, output power approaches 5.1 W beyond a center-leg length of 1000 mm and 3.25 W beyond a side-leg length of 700 mm. Within the investigated ranges, center-leg and side-leg lengths of approximately 800 and 400 mm provide the best power–volume performance, respectively. Increasing side-leg height or width also improves output. A larger coil span improves output by reducing internal resistance, whereas more turns yield diminishing gains because of higher winding and eddy-current losses. These findings provide a quantitative basis for parametric design of H-shaped-core MFEHs in railway environments. Full article

This paper investigates the impact of segmented permanent magnet (PM) configurations on torque production, eddy current loss, and demagnetization in spoke-type flux-intensifying interior permanent magnet (FI-IPM) motors. While PM segmentation has been explored in conventional interior permanent magnet synchronous motors (IPMSMs) for reducing losses, its effect in flux-intensifying (FI) motors, characterized by reverse saliency, remains underexplored. To address this, five rotor designs with segmented PMs are analyzed against a baseline model using finite element analysis, maintaining identical stator and PM volume. Results show that segmentation increases reluctance torque, compensating for reduced PM torque, while simultaneously lowering eddy current loss and enhancing demagnetization resistance. These improvements validate segmented PMs as a viable strategy to enhance the durability and efficiency of FI-IPM motors for electric vehicle applications. Full article

In this paper, the magnetic properties of HDDR NdFeB powders were improved by the grain boundary diffusion of Cu₂₈Ce₇₂ alloy. The influence and mechanism of Cu₂₈Ce₇₂ alloy and heating process on the coercivity (H_cj), remanence (B_r), and maximum magnetic energy product (BH_max) of magnetic powders were investigated. The grain boundary diffusion of Cu₂₈Ce₇₂ alloy can effectively improve the H_cj, B_r and BH_max of bonded magnets, exhibiting a trend of first increasing and then decreasing with the increase in diffusion temperature and Cu₂₈Ce₇₂ addition, and the maximum values of 927 kA/m, 0.625 T and 61 kJ/m³ are obtained at the Cu₂₈Ce₇₂ content of 5.0 wt% and the heating temperature of 380 °C. The demagnetization coupling effect is increased by a continuous grain boundary phase formed by the diffusion of Ce and Cu elements into the grain boundaries, and thus the coercivity of magnetic powder is improved. During the diffusion process, Ce element diffuses along the grain boundaries to repair the defective areas around the grains and form a Ce₂Fe₁₄B phase, which improves the H_cj and B_r of magnetic powders; on the other hand, Ce element diffuses into Nd₂Fe₁₄B grains to replace the Nd crystal sites, reducing the H_cj and B_r of magnet; therefore, the H_cj and B_r of HDDR NdFeB powders are comprehensively affected by these two aspects. Full article

This paper presents a 10 kW outer-rotor field-modulated permanent magnet vernier generator tailored for low-speed direct-drive applications. It employs an outer-rotor Spoke-array configuration, which effectively mitigates the leakage flux between adjacent pole pairs. First, the topology and operating principle of the proposed generator are elaborated. Analytical calculations of key design parameters are then performed to accelerate the modeling process. A systematic parametric sweep is conducted to optimize the motor parameters, based on which a 2D finite element analysis model is established. Comprehensive FEA simulations are carried out to investigate its flux regulation capability, static and dynamic characteristics, and permanent magnet demagnetization risk. The results demonstrate that the Spoke-array permanent magnet array effectively suppresses leakage flux, achieving a volumetric power density of 387.5 kW/m³, and the no-load back electromotive force achieves a peak amplitude of 270 V with a total harmonic distortion as low as 3.7%, which is significantly higher than that of conventional permanent magnet vernier generators. Finally, a 30-slot/23-pole prototype is fabricated and tested. The experimental results show excellent agreement with the simulation predictions, validating the effectiveness of the proposed design. Full article

Rare-earth permanent magnet synchronous motors (PMSMs) are commonly used in new energy vehicles, wind power generation, and other relevant fields due to their advantages of small size and high power density. The operation of this type of motor depends on a rare-earth permanent magnet. However, the compatibility between the permanent magnet and the motor under different motor operating conditions is unclear, which is unfavorable for the subsequent selection of motor magnets. In this study, the effects of the permanent magnet on motor performance in different operational environments were analyzed. Three different magnets, namely, N-52M, N-48SH, and SmCo-28H, were selected. Two types of operational conditions were selected: the motor temperature and input current. The load torque, the magnet’s demagnetization behavior, and the magnet’s cost-effectiveness were discussed. The results indicate that the N-52M magnet was suitable for a low temperature and low input current due to its high remanence. However, the SmCo-28H magnet should be used at high temperatures and input currents due to its superior anti-demagnetization properties. The results obtained in this study will enable comparison of the effects of different permanent magnet materials on the motor, thereby guiding the subsequent design of PMSMs. Full article

Electric motors that use neodymium–iron–boron (Nd–Fe–B) magnets are at the forefront of global efforts to reduce greenhouse gas emissions. However, a major problem associated with these motors is thermal demagnetization driven by eddy current (EC) losses in the magnets; the relatively low electrical resistivity of Nd–Fe–B magnets means that the magnetic fields in the motor generate considerable EC losses. In this study, Nd–Fe–B magnets with 0–20 wt% Si additives were produced through hot pressing to investigate the effects of Si addition on magnetic properties and electrical resistivity. Small amounts of Si significantly increased electrical resistivity without negatively affecting the magnetic properties. The high coercivity of the Nd–Fe–B magnets, 12.5 kOe, did not decrease even in the presence of up to 15 wt% Si content. The electrical resistivity of Nd–Fe–B magnets increased monotonically as the Si content increased, from 1.43 μΩm for pure Nd–Fe–B magnets to 8.17 μΩm with 20% Si. As the electrical resistivity increased, the associated EC losses decreased; the estimated EC losses were halved with the addition of ~8 wt% Si, and further decreased to one-third through the addition of ~12 wt% Si, while simultaneously maintaining high coercivity. Full article

Accurate demagnetization fault diagnosis is critical to ensuring the safety and reliability of permanent magnet synchronous motors (PMSMs). However, the number, location, and severity of demagnetized permanent magnets are mutually coupled, leading to a combinatorial explosion of fault patterns. Existing methods are largely limited to idealized assumptions involving single-magnet demagnetization or uniform demagnetization of multiple magnets, making it difficult to characterize the random nature of demagnetization in practical operation. Thus, this paper proposes a precise demagnetization fault diagnosis method based on a novel search coil (SC) configuration, in which only two toroidal-yoke-type search coils are installed in the stator slots. The proposed method partitions the rotor permanent magnets into several modules and categorizes the infinite demagnetization fault patterns into 26 representative patterns, effectively addressing the issue of fault mode explosion. Theoretical analysis and experimental results show that the voltage waveforms of the search coil over a single electrical period exhibit significant and stable differences across the identified patterns. By constructing feature vectors based on these differences, a physically interpretable mapping between the feature vectors and fault patterns is established. Combined with a corresponding pattern recognition algorithm, the proposed method enables fast and accurate differentiation of the 26 patterns without the need for complex machine learning models, thereby achieving precise localization of demagnetized permanent magnets. Simulation and experimental results verify the correctness and effectiveness of the proposed method. Full article

This paper proposes a fault-tolerant optimal design method for quad-winding permanent magnet synchronous motors (PMSMs) considering irreversible demagnetization under fault conditions. In quad-winding motors, when one or more winding sets become unavailable, the remaining windings must carry higher current to maintain the required torque. This increases the external magnetomotive force acting on the permanent magnets and may cause irreversible demagnetization, particularly in spoke-type magnet structures. To address this issue, the demagnetization characteristics of the quad-winding motor were analyzed under healthy and faulty operating conditions. Based on this analysis, an optimization process using a Radial Basis Function–Multi-Layer Perceptron (RBF–MLP) surrogate model and a combination of grid-based search and local optimization was applied to obtain an optimal motor design. The optimization results show that the irreversible demagnetization ratio was reduced from 5.9% to 0.5% while maintaining a similar magnet volume. The proposed design approach effectively suppresses irreversible demagnetization in quad-winding PMSMs. Full article

Transformers serve as crucial hubs for power transmission, but during no-load energization, the nonlinear magnetization of their cores frequently induces extreme magnetizing inrush currents. Current suppression methods encounter challenges regarding transient feature extraction and excessive circuit complexity. To overcome these limitations, this study develops a high-fidelity model of a 100 kVA transformer using MATLAB/Simulink to investigate the interaction between residual flux and the closing angle. Extensive simulations were executed across a closing phase angle range of 0° to 360° and a residual flux domain of −0.8 p.u. to 0.8 p.u. Furthermore, this study utilizes Wavelet and Clarke transforms to extract characteristic parameters and quantitatively analyze the transients within the energy domain, enabling a multi-scale assessment of the mitigation efficacy based on these extracted features. The analytical results demonstrate that an optimal pre-magnetization distribution of −0.8 p.u. for Phase A, 0 p.u. for Phase B, and 0.8 p.u. for Phase C, coupled with a target closing angle of 330°, achieves the best suppression. This strategy strictly clamps the peak inrush current to 1.5 times the rated current, significantly outperforming conventional demagnetization alone. Consequently, this highly pronounced mitigation effect provides robust support for reliable transformer protection and overall power grid security. Full article

To address the difficulty of detecting demagnetization severity in permanent magnet synchronous motors (PMSMs), this paper proposes a demagnetization severity detection method based on temperature signal and Convolutional Neural Network (CNN). First, the differences between local demagnetization and eccentricity fault in stator current harmonics are analyzed from an electromagnetic perspective, and fast Fourier transform (FFT) is used for frequency-domain analysis of the stator current to identify local demagnetization faults. On this basis, an electromagnetic–thermal coupling model is established by considering motor losses and heat dissipation boundary conditions to obtain the winding temperatures under different demagnetization severities and operating conditions. Furthermore, the temperature time series, together with speed and load torque, is constructed into a three-dimensional state space, and the proposed Conditionally Modulated Multi-Scale Convolutional Neural Network (CMSCNN) is introduced for feature learning to achieve demagnetization severity detection. Experimental results show that the proposed method achieves an average detection accuracy of 98.06% on the simulation test set and outperforms the baseline CNN model. On measured data collected from the faulty prototype, the average detection accuracy reaches 93.34%, verifying the effectiveness of the proposed method for demagnetization severity detection. Full article