Search Results (868)

This paper presents an electromechanical coupling model integrating an equivalent magnetic network (EMN) model of a dual three-phase permanent magnet synchronous motor (DTP-PMSM) with the dynamic model of a helical planetary gear transmission system. Using this model, this study analyzes the dynamic characteristics of an electric drive system, specifically motor phase current, electromagnetic torque, and gear meshing force, under self-fault-tolerant control strategies. Simulation and experimental results demonstrate that the self-fault-tolerant control strategy enables rapid fault tolerance during open-phase faults, significantly reducing system fault recovery time. Meanwhile, compared to the open-phase faults conditions, the self-fault-tolerant control effectively suppresses most harmonic components within the system; only the second harmonic amplitude of the electromagnetic torque exhibited an increase. This harmonic disturbance propagates to the gear system through electromechanical coupling, synchronously amplifying the second harmonic amplitude in the gear system’s vibration response. This study demonstrates that self-fault-tolerant control strategies significantly enhance the dynamic response performance of the electric drive system under open-phase faults conditions. Furthermore, this study also investigates the electromechanical coupling mechanism through which harmonics generated by this strategy affect the gear system’s dynamic response, providing theoretical support for co-optimization electromechanical coupling design and fault-tolerant control in high-reliability electric drive transmission systems. Full article

In this article, we propose an equivalent 2-D finite element analysis (FEA) process considering the 3-D flux of an outer rotor permanent magnet synchronous motor (PMSM). In the motor, 3-D flux such as axial leakage flux (ALF) and overhang fringing flux (OFF) are influenced based on design variables. Three-dimensional FEA is required to consider the components of 3-D flux. However, 3-D FEA is inefficient to use during the design process because of time-consuming. Therefore, we propose an equivalent FEA that considers the 3-D flux. First, the effects of ALF and OFF according to design variables such as rotor inner and outer diameter, stack length, and overhang length. Second, the 3-D flux is converted into a coefficient. Finally, it is applied to 2-D FEA. Using the proposed process, motor performance considering 3-D flux can be quickly predicted. The proposed performance prediction process is verified through simulation and experiment. Full article

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model was established, and the cogging torque was analytically calculated to compare the motor’s performance under different groove schemes. Secondly, global multi-objective optimization of the rotor groove dimensions was performed using a combined simulation approach involving Maxwell, Workbench, and Optislang, and the optimal rotor groove size structure was selected using the TOPSIS method. Finally, a comparative analysis of the motor’s performance under both rated-load and no-load conditions was conducted for the pre- and post-optimization designs, followed by verification of the mechanical strength of the optimized rotor structure. The research results demonstrate that the combined optimization approach utilizing the genetic algorithm and the TOPSIS method significantly enhances the torque characteristics of the motor. The computational results indicate that the average torque is increased to 165.32 N·m, with the torque ripple reduced from 28.37% to 13.32% and the cogging torque decreased from 896.88 mN·m to 187.9 mN·m. Moreover, the total distortion rates of the air-gap magnetic flux density and the no-load back EMF are significantly suppressed, confirming the rationality of the proposed motor design. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

Bearingless interior permanent magnet synchronous motors (BIPMSMs) have received considerable attention in recent research due to their advantages of high speed, high power density, and absence of mechanical wear. In order to improve the torque and suspension performance of the BIPMSM, an optimization design method of BIPMSM is proposed in this paper based on sensitivity analysis, response surface fitting, and the multi-objective whale optimization algorithm (MOWOA). Firstly, the structure and operation principle of the BIPMSM are introduced. Secondly, significant variables are extracted based on sensitivity analysis. Then, regression equations of the significant variables and optimization objectives are fitted by the response surface method, and global optimization is performed with MOWOA. Finally, the motor performance before and after optimization is compared. The results demonstrate that the proposed multi-objective optimization design scheme can significantly improve the performance of the BIPMSM and effectively shorten the design cycle. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Distributed energy resource systems offer improved energy utilization and reduced transmission losses by decentralizing power generation and load management. However, the power quality is often compromised by inefficient customer-side equipment, such as single-phase induction motors, which suffer from low efficiency and poor power factor. To address this issue, this paper proposes a permanent magnet retrofitting method for single-phase induction motors, which replaces the squirrel-cage rotor with a permanent magnet rotor while preserving the original stator and winding structure. The proposed method aims to enhance motor performance without significant structural changes. A single-phase induction motor was retrofitted using the proposed method, and its performance was evaluated through finite element simulations to verify the effectiveness of the design approach. This study also investigated the key factors influencing motor starting performance after the introduction of permanent magnets. This study presents a practical and effective method for the permanent magnet retrofitting of single-phase induction motors, which contributes to improving motor efficiency and enhancing power quality in distributed energy resource systems. Full article

To overcome the limitations of single-objective optimization in permanent magnet synchronous motor (PMSM) performance enhancement, this study proposes an adaptive moving least squares (AMLS) for a 12-pole/36-slot built-in PMSM. Through comprehensive exploration of the design space, a systematic approach is established for holistic motor performance improvement. The Gaussian weight function is modified to improve the model’s fitting accuracy, and the decay rate of the control weight is optimized. The optimal adaptive meta-model for the built-in PMSM is selected based on the coefficient of determination. Subsequently, sensitivity analysis is conducted to identify the parameters that most significantly influence key performance indicators, including torque ripple, stator core loss, electromagnetic force amplitude, and average output torque. These parameters are then chosen as the optimal design variables. A multi-objective optimization framework, built upon the optimal adaptive meta-model, is developed to address the multi-objective optimization problem. The results demonstrate increased output torque, along with reductions in stator core loss, torque ripple, and radial electromagnetic force, thereby significantly improving the overall performance of the motor. Full article

Aiming at the problems of low thermal analysis efficiency and high computational cost of traditional computational fluid dynamics (CFD) methods for modular fault-tolerant permanent magnet synchronous motors (MFT-PMSMs) under complex working conditions, this paper proposes a fast modeling and calculation method of motor temperature field based on a high-efficiency and high-precision network algorithm. In this method, the physical structure of the motor is equivalent to a parameterized network model, and the computational efficiency is significantly improved by model partitioning and Fourth-order Runge Kutta method. The temperature change of the cooling medium is further considered, and the temperature rise change of the motor at different spatial positions is effectively considered. Based on the finite element method (FEM), the space loss distribution under rated, single-phase open circuit and overload conditions is obtained and mapped to the thermal network nodes. Through the transient thermal network solution, the rapid calculation of the temperature rise law of key components such as windings and permanent magnets is realized. The accuracy of the thermal network model was verified by using fluid-structure coupling simulation and prototype test for temperature analysis. This method provides an efficient tool for thermal safety assessment and optimization in the motor fault-tolerant design stage, especially for heat capacity check under extreme conditions and fault modes. Full article

The Halbach array permanent magnet can improve the power density of motors. This paper uses analytical modeling to analyze and optimize the Halbach array permanent magnet synchronous motor (PMSM). Firstly, a general motor model is established to obtain the air gap flux density. Secondly, the flux linkage and back electromotive force (EMF) were calculated. The analytical results are consistent with the finite element model (FEM) results. Thirdly, the effects of slot opening, magnetization angle, and main magnetic pole width on air gap flux density and back-EMF were studied. Finally, based on the optimization results, a prototype was manufactured, and performance testing was conducted successfully. Verification of the back-EMF of the prototype shows that the relative errors between FEM and the measured values are 1.1%, and the relative errors between the analytical values and measured values are 1.6%, which verifies the accuracy of the proposed analytical modeling. The proposed analytical model is universal and can be used to quickly adjust the magnetization form, magnetization angle, and pole width without remodeling in the finite element software, which is convenient for optimizing parameters in the early stage of motor design. Full article

This study presents an analysis of the electromagnetic noise and vibration in a surface-mounted permanent magnet synchronous machine (SPMSM), focusing on their excitation sources. To investigate this, the excitation sources were identified through an analytical approach, and their effects on electromagnetic noise and vibration were evaluated using a finite element method (FEM)-based analysis approach. Additionally, an equivalent curved-beam model based on three-dimensional shell theory was applied to determine the deflection forces on the stator yoke, accounting for the tooth-modulation effect. The stator’s natural frequencies were derived through the characteristic equation in free vibration analysis. Modal analysis was performed to validate the analytically derived natural frequencies and to investigate stator deformation under the tooth-modulation effect across various vibration modes. Furthermore, noise, vibration, and harshness (NVH) analysis via FEM reveals that major harmonic components align closely with the natural frequencies, identifying them as primary sources of elevated vibrations. A comparative study between 8-pole–9-slot and 8-pole–12-slot SPMSMs highlights the impact of force variations on the stator teeth in relation to vibration and noise characteristics, with FEM verification. The proposed method provides a valuable tool for early-stage motor design, enabling the rapid identification of resonance operating points that may induce severe vibrations. This facilitates proactive mitigation strategies to enhance motor performance and reliability. Full article

This paper proposes a harmonic screening-based method for calculating the cogging torque of the axial flux permanent magnet synchronous motor. The magnetic field energy in the air gap is derived from the air gap flux and the magnetomotive force of rotor. The cogging torque is then obtained using the energy-based method. Compared with finite element analysis, the proposed approach is significantly faster while maintaining high accuracy. It is particularly effective for scenarios involving stator staggering, which can facilitate quick calculation of cogging torques of many different staggering angles, offering rapid insights into motor performance during the initial design. The method achieves a similarity accuracy with FEA results and reduces computation time, demonstrating both its efficiency and reliability. Full article

In this study, we present the design and electromagnetic performance optimization of a micro-electromechanical system (MEMS) miniature outer-rotor permanent magnet motor. With increased attention towards higher torque density and lower torque pulsations in MEMS micromotor designs, an adaptation of an external rotor can be highly attractive. However, with the design complexity involved in such high-performance MEMS outer-rotor motor designs, the ultra-miniature 3D coil structures and the thin-film topology surrounding the air gap have been one of the main challenges. In this study, an ultra-thin outer-rotor motor with 3D MEMS silicon-based coils and a MEMS-compatible manufacturing method for the 3D coils is presented. Additionally, finite element simulations are conducted for the thin-film topology around the air gap to optimize performance characteristics such as torque developed, torque pulsations, and back electromotive force amplitude. Ultimately, the average magnetic flux density increased by 37.1%, from 0.361 T to 0.495 T. The root mean square (RMS) value of the back EMF per phase rises by 14.4%. Notably, the average torque is improved by 11.3%, while the torque ripple is significantly reduced from 1.281 mNm to 0.74 mNm, corresponding to a reduction of 49.9% in torque ripple percentage. Full article