Jinghong Zhao

Accurate loss evaluation of high-frequency transformers (HFTs) under non-sinusoidal excitation is essential for the optimal design of dual-active-bridge (DAB) converters, yet remains challenging due to spatially non-uniform magnetic flux distribution across the core and spectrally rich harmonic content in the winding current. This paper proposes a dual-refinement electromagnetic loss analysis method that combines a topology-aware region-partitioned improved generalized Steinmetz equation (IGSE) with a harmonic-resolved Dowell model. The ferrite core of a shell-type HFT is partitioned into three magnetically distinct zones—core legs, side pillars, and yokes—with the IGSE applied independently to each zone using zone-specific flux density waveforms derived from the single-phase-shift (SPS) control model, thereby capturing spatial non-uniformity in core loss. For winding loss, the current is decomposed via fast Fourier transform, and per-harmonic AC resistance is computed up to the seventh order with frequency-dependent skin depths, accounting for the spectral contribution to copper loss. A 300 V/6 kVA ferrite-core foil-winding HFT prototype was fabricated, and the proposed method was validated through COMSOL finite element simulations and experimental measurements using open-circuit and short-circuit tests. Results show that the calculated core loss and winding loss agree with measurements within 10–15% and 8–10%, respectively, demonstrating improved accuracy over the conventional uniform-IGSE and total-RMS Dowell approaches.

Aiming at the chattering issue in speed observation for sensorless control, this paper proposes a sliding mode observer based on an improved double-power reaching law for high-performance speed estimation in five-phase induction motors. Traditional constant-rate reaching law observers exhibit significant chattering, while the double-power reaching law, though offering certain “variable-gain” adjustment effects, still has limited chattering suppression capability. To address this, the paper introduces a state variable related to the stator current into the conventional double-power observer, further enhancing the ability of the sliding mode gain to vary with the system state. This approach effectively suppresses chattering while maintaining convergence speed. The stability of the observer system employing the new reaching law is proven using Lyapunov stability theory, and the value ranges of key parameters are determined. Simulation results demonstrate that, compared to traditional constant-rate reaching law and conventional double-power reaching law observers, the proposed improved method significantly reduces speed observation chattering and effectively enhances the observation accuracy of the observer.

This paper presents a novel Sensitivity-Guided Two-Stage Optimization (SGTSO) framework for magnetic gear (MG) design, introducing two fundamental methodological advances: (1) the adoption of global Sobol sensitivity analysis, which transcends conventional local sensitivity techniques by holistically quantifying both individual parameter effects and the interactions across the complete design space, and (2) the establishment of a mathematically guaranteed convergent two-stage optimization methodology that strategically decomposes high-dimensional problems into sequential subproblems. Unlike traditional one-factor-at-a-time sensitivity approaches that overlook parameter interdependencies, Sobol indices deliver quantitative evaluation of individual parameter contributions and coupling effects. The two-stage optimization architecture is rigorously proven to converge to near-optimal solutions under weak parameter coupling assumptions, with mathematically derived error bounds The optimized configuration achieves remarkable performance features: 65.4% suppression of inner rotor torque ripple, 27.2% reduction in outer rotor torque ripple, and 19.2% decrease in Permanent Magnet (PM) utilization, while preserving average torque output within a marginal 4.03% reduction. The proposed framework achieves a 5.25-fold enhancement in computational efficiency while maintaining mathematical convergence assurance, marking a substantial progression beyond conventional heuristic optimization paradigms.

This paper proposes a novel optimization framework for Magnetic Gear Compound Machines (MGCMs) that integrates parameter grouping and surrogate modeling to address challenges of high-dimensional design spaces and conflicting objectives. The core methodological contribution is a new parameter grouping strategy employing sensitivity analysis and partial correlation coefficients, which systematically classifies design parameters into high-, medium-, and low-impact groups. This approach achieves a 60% reduction in optimization dimensionality while preserving essential electromagnetic relationships. Latin Hypercube Sampling (LHS) is coupled with high-fidelity Maxwell 2D transient simulations to construct an accurate Kriging surrogate model, which is then integrated with the NSGA-III algorithm for efficient Pareto front identification. Comprehensive simulations demonstrate the framework’s exceptional performance. The sensitivity-based optimized design achieves an 85.5% reduction in inner rotor torque ripple (0.091), maintains 90.3% of the original torque output (475.100 N·m), and preserves 94.8% of the induced electromotive force (399.578 V), yielding an optimal objective function value of −0.901 that indicates superior overall performance improvement. In comparison, the correlation-based approach provides an 84.5% torque ripple reduction (0.097) with 97.7% torque retention (514.166 N·m) and 86.0% voltage preservation (362.739 V), corresponding to an objective function value of −0.841. Both grouping strategies significantly reduce computational cost by approximately 60% compared to conventional single-stage optimization methods. This research establishes an effective optimization paradigm for MGCMs, successfully resolving the fundamental trade-off between power density maximization and operational stability, with promising applications in electric propulsion and renewable energy systems.

High-speed solid rotor induction motors (HS-SRIMs) are favored for their robust structure but suffer from significant eddy current losses at high speeds, leading to efficiency reduction and thermal challenges. This study establishes a comprehensive multi-objective optimization framework to address this issue. The eddy current loss characteristics are first investigated using finite element analysis (FEA), focusing on the impact of key parameters like air gap length and rotor slotting. A sensitivity analysis quantifies their influence on motor performance. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed for multi-objective optimization, aiming to minimize eddy current loss while maximizing efficiency and electromagnetic torque. The optimization results demonstrate a significant improvement: a reduction in eddy current loss of 59.8%, an increase in efficiency of 17.2%, and a boost in output torque of 50.8%. Coupled electromagnetic–thermal simulations further validate a substantial decrease in operating temperatures. The proposed method provides an effective design approach for high-performance HS-SRIMs.

Solid rotor induction motors have a solid body rotor, which leads to low efficiency and power factor, and currently, the rotor is mostly optimized by slotted and squirrel cage structures. A generalized multilayer analytical model for different rotor structures is established, which can consider the effects of rotor eddy currents and saturation, based on which a generalized equivalent circuit model is established. The effects of number of slots, depth of slots, width of slots, squirrel cage material and end ring thickness on rotor impedance, torque and rotor losses are analyzed. On this basis, with efficiency, power factor, starting torque and starting current as the optimization objectives, and the number of slots, slot depth, slot width, squirrel cage material and end ring thickness as the optimization variables, the optimization schemes of slotted rotor and squirrel cage rotor are obtained by using the three-dimensional finite element method. The theoretical analysis is verified by finite element simulation and prototype experiment, and the results show that the electromagnetic parameters of solid rotor induction motors with different rotor structures can be accurately calculated using the universal magnetic field analytical model and the universal equivalent circuit model with an error within 5.8%. Slotted and squirrel cage rotors can effectively improve the motor power factor and efficiency, but this will lead to a decrease in starting performance. For the optimization function established in this paper, compared with the smooth rotor, the performance of the squirrel cage rotor is improved by 6.08%, which verifies the accuracy and validity of this paper and the optimization design scheme.

Direct-drive permanent magnet synchronous machines (DDPMSMs) have recently become an ideal candidate for applications such as military, robotics, electric vehicles, etc. These machines eliminate the need for a transmission mechanism and excitation coil circuits, which enhances the system’s overall efficiency and decreases the likelihood of failures. However, it may incur demagnetization faults. Due to the characteristic of having a large number of pole pairs, this type of machine exhibits numerous demagnetization fault modes, which poses a challenge in locating demagnetization faults. This paper proposed a probabilistic neural network (PNN)-based diagnostic system to detect and locate demagnetization faults in DDPMSMs, using information obtained through three toroidal-yoke-type search coils arranged at the bottom of the stator slot. A rotor partition method is proposed to solve the problem of demagnetization fault location difficulty caused by various fault modes. Demagnetization fault location is achieved by sequentially diagnosing the condition of each partition of permanent magnets. Three demagnetization fault identified signals (DFISs) are constructed by the voltage of the three toroidal-yoke coils, which are used as inputs of PNNs. Three PNNs have been designed to map the extracted features and their corresponding types of demagnetization faults. The database for training and testing the PNNs is generated from a DDPMSM with different demagnetization conditions and different operating conditions, which are established through an experimentally validated mathematical model, an FEM model, and experiments. The simulation and experimental test results showed that the accuracy in diagnosing the location of the demagnetization fault is 99.2% when the demagnetization severity is 10%, which demonstrates the effectiveness of the proposed three PNN-based diagnostic approach.

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor’s speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance.

Hybrid excitation generators combine the advantages of electric excitation motors and permanent magnet generators. Focusing on a permanent magnet generator as the object of study, an auxiliary excitation winding is introduced for voltage regulation. The main magnetic field is established by the permanent magnet, and the auxiliary excitation winding provides the magnetic potential required to regulate the air-gap magnetic field. While improving the voltage regulation performance of permanent magnet generators, it also reduces the loss of excitation windings in electrically excited generators. Based on a hybrid excitation generator with dual excitation windings, in the following article, we present a hybrid excitation generator equivalent to a full permanent magnet motor with the minimum output voltage, and an accurate subdomain model of the full permanent magnet motor is established considering the influence of slot opening. By establishing a matrix, the distribution curve of air-gap magnetic density was solved and ultimately verified using finite element analysis. The results of the present study lay a solid foundation for solving the air-gap magnetic density distribution of various parts of the hybrid excitation generator and studying its performance in the future.