Magnetostriction in non-oriented electrical steel constitutes the primary excitation mechanism for vibration in motor stator cores. While magnetostrictive anisotropy stems from a combination of intrinsic material characteristics and extrinsic factors-including grain size heterogeneity, residual stresses, and crystallographic texture-the manufacturing processes of stator cores introduce significant additional residual stresses. These process-induced stresses further degrade magnetostrictive performance. Prevailing research, predominantly conducted on unprocessed silicon steel laminations under idealized conditions, has substantially underestimated magnetostrictive behavior in operational cores. To address this limitation, this study implemented a magnetostriction measurement system utilizing resistance strain gauges. Comprehensive characterization was performed on stator cores manufactured from 25SW1300-grade steel, with particular emphasis placed on quantifying the anisotropy mechanisms and the specific influence of manufacturing processes. Obtained results found that the stator core loss exceeded that of single-piece silicon steel sheets by more than 8%. Critically, localized magnetostrictive strain was found to be amplified by welded operations by 3 mu m/m, representing a twofold increase relative to non-welded regions.

PurposeThis paper aims to propose an improved parameter identification method for the inverse play model to reduce the error in hysteresis simulation.Design/methodology/approachThis paper proposes an improved parameter identification method for the inverse play model, which selects a portion of the data from numerically generated first-order reversal curves to identify the shape function, thereby avoiding simulation errors caused by repeated hysteresis operators. The proposed method is then combined with an optimization algorithm to derive the optimal parameter solution for the model, enabling hysteresis simulation.FindingsBy using the proposed method to identify the parameters of the inverse play model and simulate hysteresis loops, it is demonstrated that the global error in hysteresis loss calculation is controlled within approximately 5%. This indicates a significant improvement in the accuracy of hysteresis simulation and also addresses the issue of non-smooth hysteresis loops.Originality/valueThe proposed method significantly improved the accuracy of hysteresis simulation with the inverse play model after parameter identification. Meanwhile, due to the reduced amount of data used for identifying the shape function, the efficiency of obtaining the optimal solution was increased, thereby shortening the computation time.

Due to the hysteresis phenomenon of ferromagnetic materials, an unknown remanence BR will be generated after the material is disconnected from the power supply, which can cause a considerable inrush current, so that the safety of electrical equipment can be seriously affected. Therefore, various methodologies for evaluating the remanence have been proposed to achieve more effective demagnetization of ferromagnetic materials. However, the proposed remanence evaluation only applies in certain exceptional cases due to the lack of physical significance. In this study, based on the maximum irreversible differential magnetization [max(dMirr/dH), MIDM], a new remanence evaluation method is proposed. First, based on the Jiles-Atherton (J-A) hysteresis theory, a relationship between the MIDM and the remanence is investigated. Then, hysteresis loops under the different remanence are used to obtain the MIDM for the remanence estimation in practice. Finally, an experimental platform based on the Epstein frame is used to examine the reliability of this approach. The obtained results indicate that the proposed method has an error range of 2.72%-9.67% in experiments. This study provides a new theoretical basis for the remanence evaluation, which has essential scientific theoretical significance.

This paper proposes a vibration reduction method of switched reluctance motor with amorphous alloy core (SRMA) based on multi-material topology optimization. Firstly, the properties of negative magnetostrictive material are used to SRMA stator core to offset vibration induced by radial electromagnetic force and magnetostrictive force. The Young’s modulus and relative permeability in the optimized region are expressed as continuous functions of the double design variables. Then, the double design variables interpolation function is coupled to the multi-material topology optimization to determine the distribution and boundary structure of the mixed core material. Considering the multi-dimensional constraints in the optimization process are difficult to converge, the augmented Lagrangian method is used to improve the calculation speed. Finally, the proposed method is verified through experimental testing and simulation analysis. The results show that under the premise of ensuring the normal operation of SRMA, the vibration displacement amplitude of the optimized stator core is reduced by 36%. The calculation time of multi-material topology optimization is reduced by 30%.

Purpose This paper aims to propose a novel global optimization algorithm for fast and precise parameter identification of the inverse Preisach hysteresis model. Design/methodology/approach An enhanced parallel Runge-Kutta (ERUN) algorithm is proposed to identify the nine-parameter inverse Preisach model. Integrates chaotic mapping, parallel processing and adaptive perturbation to strengthen global exploration and convergence robustness. The Preisach model used in this paper is established by analytically deriving the inverse Everett function from first-order reversal curves (FORCs) and validated against experimental hysteresis loops of 20SW1200 nonoriented (NO) silicon steel, B30P105 grain-oriented silicon steel and 50WW470 NO silicon steel. Findings The ERUN algorithm achieves a 1.83% error and a computation time of 40.6 s, outperforming the genetic algorithm, simulated annealing, particle swarm optimization and the original Runge-Kutta (RUN) optimization method. The average relative root mean square error analysis confirms that all simulated hysteresis loop errors remain below 10%, even at low magnetic flux densities. Originality/value The proposed ERUN algorithm extends the original RUN method by introducing chaotic mapping and parallel computing strategies, effectively alleviating the premature convergence problem. This work presents a progressive improvement to the global optimization algorithm for inverse Preisach hysteresis model parameter identification, extending the original RUN optimizer with chaotic mapping and parallel computing strategies to achieve faster convergence and higher accuracy, achieving the lowest parameter estimation error (1.83%) and the shortest computational time (40.6 s) among the benchmarked algorithms.

To overcome computational inefficiency, post-processing limitations, and weak generalization in conventional U-Nets for permanent magnet motor field prediction, this paper proposes an improved numerical U-Net model. The approach replaces the output sigmoid activation with a linear function and utilizes numerical 2D arrays instead of images to facilitate direct value extraction. Generalization is enhanced through a dual-path encoder structure incorporating a dedicated excitation input channel and Dropout layers, facilitating concurrent multi-angle predictions. Computational acceleration is achieved via optimized convolution kernels. Validated on Prius PM motor vector field data, the model demonstrates an 11% accuracy increase, training speeds 1.5 times faster, and prediction speeds 1.3 times faster than standard U-Nets. Additionally, it achieves an average flux density error of below 4%, with single-field computation speeds 15 times faster than those of finite element methods.

This study presents a stress-dependent magnetostriction model that incorporates hysteresis effects to simulate the magnetostriction properties of amorphous alloys in response to variations in magnetic fields and stress. The model is grounded in a microscopic statistical constructive framework. Given the absence of a crystalline structure in amorphous materials, a probability distribution function is developed to account for stress effects, which characterizes the randomly distributed magnetic moments within the amorphous matrix through the concept of locally ordered magnetic moment regions. The relationship between magnetostriction and magnetization is established by integrating the derived stress-dependent magnetostriction expression with a modified anhysteretic magnetization model. Hysteresis effects are addressed by incorporating an irreversible magnetization component, as informed by the inverse Jiles-Atherton theory. The parameters of the model are calibrated using experimental data. The simulation outcomes demonstrate that the proposed model successfully replicates the magnetostriction loop while accounting for stress influences.

To address nonlinearity-induced waveform distortion and slow convergence in the magnetic characterization of electrical steels, a parameter-free controller is proposed in this work, which enhances convergence speed while maintaining tracking accuracy and avoiding parameter dependence via adaptive compensation mechanisms. The controller consists of two primary components: the first incorporates polynomial fitting for rapid evaluation, which considerably reduces the number of iterations; the second employs a strategy that decouples frequency-domain and time-domain control. This strategy initially normalizes the waveform, then independently modulates its amplitude and shape, thus enabling rapid and precise waveform control. The controller has been successfully implemented in three one-dimensional (1-D) magnetic measurement systems. The results demonstrate that through assessment of the initial excitation in magnetic measurements of grain-oriented silicon steel, the initial waveform error is reduced to less than 3%, the harmonic content of the final waveform is controlled to approximately 0.2%, and the root mean square error of the waveform is maintained below 0.5%. Furthermore, magnetic property measurements were conducted over a frequency range from 2 Hz to 2000 Hz, with multiple loss values obtained at different frequencies for uncertainty analysis.

The current underutilization of existing physics-informed neural networks (PINN) in the domain of electromagnetic fields necessitates a concerted effort to facilitate the gradual transition from finite element methods to PINN. The complexity of simulating electric fields across various media using PINN presents significant challenges, particularly when addressing multiple equations and media in the context of magnetic field analysis, where numerous magnetic field quantities must be resolved. This study employs PINN to achieve the solution of the electric field at a simple boundary, enabling the simulation of electric fields in diverse media by accounting for varying conductivity. Additionally, the research addresses the dynamics of electric field solutions in response to time-varying boundaries. Subsequently, multiple equations are encoded concurrently, and boundary conditions are established to facilitate the simultaneous prediction of multiple magnetic fields. Ultimately, the methodology addresses the resolution of vector magnetic fields in nonlinear materials across multiple media. The accuracy of the proposed approach is corroborated through a comparative analysis with the finite element method.

To achieve both controllable output and high misalignment tolerance in inductive power transfer (IPT) systems, an IPT system based on parallel dual-channel hybrid topology is proposed. First, a dual-vertical magnetic coupling structure is designed based on the self-decoupling theory of orthogonal windings, effectively solving the cross-coil coupling interference problem. Next, the LCC/S and LCC/LCC topologies are used to form an input-parallel-output-parallel hybrid topology to realize dual-channel parallel constant-current output. To enhance the output current regulation and misalignment tolerance, a dual-topology output collaborative optimization strategy is proposed. The stability factor α and regulation factor β are introduced to control the output gains of the LCC-S and LCC-LCC topologies, respectively. An output current regulation mechanism based on α and β is established, and a velocity-controlled particle swarm optimization (VCPSO) algorithm is adopted to optimize the current fluctuation ratio, solving for the optimal α and minimizing output current fluctuations within a 140mm offset range. Experimental results show that under zero-phase input, 200% load variation, and 50% offset conditions, the system output current is stabilized within the preset range of 5.5A to 6.5A, with a maximum fluctuation rate of less than 7%.

Purpose This paper aims to propose a novel parameter and topology combination optimization method of switched reluctance motors with an amorphous alloy core (SRMA), which can take into account the vibration and torque of SRMA and achieve multi-objective performance improvement. Design/methodology/approach First, the stator pressing structure is designed according to the force− magnetic coupling relationship, which is based on the inverse-magnetostrictive effect by parameter optimization, and the rotor structure is designed according to a solid isotropic with material penalization-based method by topology optimization. Second, a novel parameter and topology combination optimization model is established. Then, the optimal pressure value of stator teeth and the optimal rotor structure are obtained by the proposed method. Finally, the novel parameter and topology method is verified by simulations and experiments. Findings The results verify the efficacy of the proposed method and the accuracy of the proposed numerical analysis model. The magnetostriction effect and its inverse effect cannot be ignored in motor design. Compared with the motor before optimization, the average torque is increased by 11%, the maximum vibration displacement is reduced by 37% and the torque ripple is not increased. Originality/value The novel parameter and topology combination optimization method proposed in this paper takes into account the magnetostrictive effect and its inverse effect of motor core materials and establishes an electromagnetic-mechanical coupling numerical analysis model of SRMA, which improves the multi-objective performance of motor.

Accurate iron loss calculation in electrical equipment under non-sinusoidal excitation necessitates stable finite element methods incorporating dynamic hysteresis properties. First, this paper addresses numerical instability at high flux densities by integrating an exponential extrapolation technique into the inverse Preisach hysteresis model, preventing divergence beyond the Everett function limit. Second, memory effects during magnetization are considered by tracking extreme states in time-domain finite element analysis. Meanwhile, to improve convergence near reversal points, a correction method of reluctivity at reversal points is proposed. Finally, comparisons of experimental and computational results on an Epstein frame demonstrate that the proposed time-domain finite-element scheme reduces the average computation time at reversal points by 40% compared to conventional methods, while maintaining high accuracy in magnetic field and loss calculations, significantly improving the overall convergence rate.

Incorporating magnetic hysteresis in time-stepped finite element analysis is still challenging as the reluctivity exhibits a discontinuity at the reversal points when using the fixed-point method. Solving such nonlinear problems is quite slow under non-sinusoidal excitation due to the presence of multiple reversal points. This paper proposes an improved fixed-point iteration algorithm incorporating the Local-coefficient Method(LCM) and an Enhanced Anderson Acceleration(EAA) to efficiently solve the problem of excessive iteration duration. The EAA proposed in this paper, which extends the traditional Anderson Acceleration framework, dynamically adapts the residual window size based on the local residual behavior. This adaptability enables EAA to effectively leverage iterative historical information to expedite computations and accommodate variations in nonlinearity, thereby significantly enhancing the overall computational efficiency. Simulations and experiments under sinusoidal and non-sinusoidal excitations confirm that the proposed iteration algorithm can improve computational efficiency while ensuring stability.

As a consequence of the hysteresis phenomenon in transformer cores, an indeterminate remanence B-R will persist in iron cores after the transformer is deactivated from the power grid. The remanence is the primary cause of inrush current during the no-load switching of transformers. Therefore, effectively evaluating transformer core remanence helps to suppress inrush current and enhance the power system's safe and stable operation. In this paper, a new remanence evaluation method based on Magnetic Barkhausen Noise (MBN) is proposed. Firstly, according to the J-A magnetization theory, a relationship between the peak voltage of MBN and the stress is obtained. Then, according to the magnetism model, a relationship between the stress and the remanence is established. Finally, based on the above relationship, the relationship between the peak voltage of MBN and the remanence is derived by using the stress as an intermediate quantity. In addition, an experimental platform based on a square iron core is used to examine the reliability of this approach in the paper. The obtained results indicate that the error of the proposed method is less than 6% in experiments. A novel theoretical framework for evaluating remanence is established in this research, which can provide important scientific and theoretical value.

Accurate and meticulous modeling of the hysteresis loops displayed by electrical steels under complexwaveforms with diverse magnetization intensities is of paramount importance for achieving the utmostexcellence in electrical equipment design.In particular, inverse hysteresis models are preferable in FEM forderiving magnetic field values from vector potential to reduce iterations. However, the prevailing inversePreisach models, which are grounded in the inverse Everett function, fall short in elucidating the intrinsicphysical nature of hysteresis phenomena associated with the magnetization process. Moreover, their generalizedmoving adaptations entail substantial computational costs when integrated with Finite Element Method (FEM)software. This study presents a generalized, analytically derived inverse Preisach model.It characterizes ananalytical Everett function for the irreversible component while explicitly incorporating both hysteresisdependence on magnetization state and reversible contributions. The resulting model guarantees accuracy forsymmetric and asymmetric minor loop simulations and enables straightforward FEM implementation.Validation with B30P105 grain-oriented silicon steel measurements across varied excitation levels confirmsmodel accuracy, with computational performance benchmarked against conventional approaches.

To reduce the vibration of a switched reluctance motor (SRM) with an amorphous alloy core, a topology optimization technology for the stator core of SRM based on the addition of negative magnetostrictive material is proposed. Firstly, the teeth of the stator of SRM are selected as the optimization regions that contain negative magnetostrictive material. Utilizing the Solid Isotropic with Material Penalization (SIMP) model, the interpolation function of the material density is used to characterize Young’s modulus and relative permeability in the optimization regions. Secondly, the magnetic-mechanical coupling topology optimization of the SRM is performed. With the elastic strain energy and static torque as constraints, and the minimization of vibration displacement as the optimization objective, the optimized lower vibration topology for a stator core filled with negative magnetostrictive material is carried out. Finally, the effectiveness of the optimized lower vibration topology is verified by simulation and experimental results. The results show that the vibration displacement of the stator of the SRM core is reduced by 21

To simplify the complexity of the control strategy during the switching process from the constant current mode to the constant voltage mode of the wireless charging system, an integrated coil wireless charging system based on a switchable hybrid structure is proposed. Through switching the compensation element at the receiving pad, constant current (CC) and constant voltage (CV) switching can be achieved under the condition of zero phase angle input (ZPA). Meanwhile, through the optimized design of the integrated coil and reasonable parameter configuration, the system can still maintain good CC-CV output characteristics when the misalignment happens. The experimental results show that the maximum fluctuation of charging current and voltage is less than 9%, and the charging efficiency can be maintained at a high level within 100 mm misalignment in the X -axis.

The traditional torque sharing function (TSF) strategy can lead to large torque ripple and copper consumption in switched reluctance motors (SRMs) due to the limitation of the voltage in the phase change interval. In this paper, an improvement scheme is proposed to address this problem. First, a segmented non-linear correction TSF (SN-TSF) is proposed. Then with the torque ripple and copper consumption of the SRM as optimization objectives, the proposed SN-TSF values are optimized using the velocity control particle swarm optimization (VCPSO) algorithm based on the particle velocity–boundary relationship. The optimal parameters are used to obtain the optimal SN-TSF curves with different loading torques and speeds. Experimental results show that the proposed SN-TSF strategy reduces the torque ripple by 40.17

Due to the magnetic domain movement and energy change under magnetic field and stress applied, the macroscopic magnetic properties and magnetostriction properties of non-oriented (NO) silicon steel are influenced. To present this effect of the magnetic domains’ motion mechanism, a stress-dependent magnetostriction must be modeled. This paper proposes an improved stress-dependent magnetostriction model (I-SDM model) for NO silicon steel based on a simplified multi-scale model. Firstly, the stress-dependent expression of the anhysteretic magnetization, obtained by assuming a six-domain structure and considering the volume fraction of magnetic domain energy, is introduced into the inverse Jiles–Atherton (JA) model. In this way, the stress-dependent hysteresis model is constructed. Then, coupling with the improved quadratic domain rotation model that considers pinning effects, the I-SDM model for NO silicon steel is built. Finally, with the parameters obtained from experimental results, the proposed model under varying stresses is simulated and compared with other models. It shows that the proposed model has the highest simulated accuracy for both λ-H loop and λ-B loop with stress applied.