Search Results (573)

This paper presents the initial design of a permanent magnet synchronous machine with mechanically controlled excitation flux using the linear sliding motion of an additional excitation source placed inside a hollow shaft in the rotor. A new rotor design concept and assembling method are described and presented in detail. On the basis of 3D-FE analysis results, the principle of adjusting reluctance, magnetic flux distribution, flux linkage, field weakening rate, no-load back EMF waveforms, electromagnetic torque, magnetic tension, and the effectiveness of the excitation adjustment of the presented machine design are discussed. The presented machine concept enables the design of permanent magnet excited machines with a good flux control range operating in changing load conditions under variable rotor speed. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

This study considers a robust design methodology to reduce cogging torque in the EPS (Electric Power Steering) of an automotive system. Cogging torque reduction is the key design factor to improve steering feeling and drive stability in an EPS system. For this reason, an SPMSM (Surface Permanent Magnet Synchronous Motor) has been widely applied to drive a motor in an EPS system. Furthermore, two design methods, which are a magnet shape and step-skew design for rotor assembly, have been mainly used to reduce cogging torque in an SPMSM. In this paper, an SPMSM is selected as the drive motor and a robust design methodology is proposed to reduce cogging torque in an EPS system. Firstly, a cycloid curve is used for the magnet shape to reduce cogging torque. An evaluation index δq is also used to compare this with a conventional magnet shape design. Secondly, based on the results of the magnet shape design with the cycloid curve, a step-skew design for rotor assembly is also applied to reduce cogging torque. In order to validate the effectiveness of the robust design for the cycloid curve and conventional magnet shape with rotor step-skew, the results from FEM (Finite Element Method) analysis and prototype tests are compared. The cycloid curve magnet shape model with rotor step-skew was verified to reduce the cogging torque and enhance the robustness for cogging torque variation through the analysis and protype test results. The verified results for the proposed model will be extended to meet the required cogging torque variation for the various applications driven by SPMSM with the robust design model. Full article

This paper presents the design of a permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) for compressor applications using Sm-series injection-molded magnets that eliminate heavy rare-earth elements. The high shape flexibility of the injection-molded magnets enables the formation of a curved multi-layer flux-barrier rotor geometry based on the Joukowski airfoil potential, optimizing magnetic flux flow under typical compressor operating conditions. Furthermore, electromagnetic performance, irreversible demagnetization behavior, and rotor stress sensitivity were analyzed with respect to key design variables to derive a model that satisfies the target performance requirements. The validity of the proposed design was confirmed through finite element method (FEM) comparisons with a conventional IPMSM using sintered NdFeB magnets, demonstrating the feasibility of HRE-free PMa-SynRM for high-performance compressor drives. Full article

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields. Full article

Model predictive control offers high-performance regulation for multiphase drives but is critically dependent on the accuracy of mathematical models for prediction, making it vulnerable to parameter mismatches and uncertainties. To achieve parameter-independent control across both healthy and faulty operations, this paper proposes a novel dynamic mode decomposition with control (DMDc)-based model predictive current control (MPCC) scheme for five-phase permanent magnet synchronous motors. The core innovation lies in constructing discrete-time state-space models directly from operational data via the open-loop DMDc identification, completely eliminating reliance on explicit motor parameters. Furthermore, an improved fault-tolerant strategy is developed to mitigate the torque ripple induced by inter-turn short-circuit (ITSC) faults. This strategy estimates the key fault characteristic, the product of the short-circuit ratio and current, through a spectral decomposition of the AC component in the q-axis current variations, bypassing the need for complex parameter-dependent observers. The derived compensation currents are seamlessly integrated into the predictive control loop. Experimental results comprehensively validate the effectiveness of the proposed framework, demonstrating a performance comparable to a conventional MPCC under healthy conditions and a significant reduction in torque ripple under ITSC fault conditions, all achieved without any prior knowledge of motor parameters or the retuning of controller gains. Full article

Line-Start Permanent Magnet Synchronous Motors (LSPMSMs) offer a hybrid solution that combines the high efficiency of permanent magnet motors with the self-starting capability of induction machines. This review examines their key performance characteristics, historical development, and design approaches. Advantages such as high efficiency, improved power factor, and operational stability are discussed alongside challenges like limited critical inertia and synchronization issues. Design enhancements through rotor topology optimization and cage resistance adjustment are also explored. Finally, market trends and economic considerations are evaluated, highlighting the strong potential of LSPMSMs in energy-efficient motor applications. Full article

This paper compares two types of interior permanent magnet synchronous motors (IPMSMs) to determine the most effective arrangement for electric vehicle (EV) applications. The comparison is based on torque ripple, power, efficiency, and mechanical objectives. The study introduces a novel technique that optimizes notching parameters in a selected motor topology by inserting a ship-shaped notch into the bridge area between double U-shaped layers. In addition, this study presents two comprehensive approaches of robust combinatorial optimization that are used in machines for the first time. In the first approach, modeling is performed to identify important variables using Pearson Correlation and the mathematical model of the Anisotropic Kriging model from the Surrogate model. Then, in the second approach, the proposed algorithm, Multi-Objective Genetics Algorithm (MOGA), and Surrogate Quadratic Programming (SQP) are combined and implemented on the Anisotropic Kriging model to choose a robust model with minimum error. The algorithm is then verified with FEM results and compared with other conventional optimization algorithms, such as the Genetics Algorithm (GA) and the Particle Swarm Optimization algorithm (PSO). The motor characteristics are analyzed using the Finite Element Method (FEM) and global map analysis to optimize the performance of the IPMSM for EV applications. A comparative study shows that the enhanced PMSM developed through the optimization process demonstrates superior performance indices for EVs. Full article

The estimation of core loss in permanent magnet synchronous machines (PMSMs) is a fundamental step for the optimization of the performance of PMSM drives. However, there is a lack of literature which fully demonstrates the goodness of some of the empirical approximations that are commonly used in industrial and automotive sectors. This work investigates how different approximations for the core loss estimation of PMSMs can lead to considerable error across the entire machine operating domain. An interior PMSM (IPMSM) is modeled in finite element analysis (FEA) and used to calibrate the coefficients of the Bertotti equation. Approximations of the Bertotti equation are then made, which are calculated from a simple algebraic expression of measurable states, and these are used to estimate machine core loss in the whole direct-quadrature ($d-q$) domain of operation. The estimated core loss obtained with the approximations are finally compared to FEA core loss results. The approximations are shown to have considerable variability in their accuracy compared to FEA results. The results of this work can be used as guidance during the development of estimation algorithms for PMSM losses and the development of control strategies. Full article

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance. Full article

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of a commercial motor–generator system in high-demand applications. A baseline model of a permanent magnet synchronous motor (PMSM) was developed using MotorCAD 2023^® software, which was supported by reverse engineering techniques to accurately replicate the motor’s physical and thermal characteristics. Subsequently, multiple cooling strategies were simulated under consistent operating conditions to assess their effectiveness. These strategies include conventional axial water jackets as well as advanced oil-based methods such as shaft cooling and direct oil spray to the windings. The integration of these systems in hybrid configurations was also explored to maximize thermal efficiency. Simulation results reveal that hybrid cooling significantly reduces the temperature of critical components such as stator windings and permanent magnets. This reduction in thermal stress improves current efficiency, power output, and torque capacity, enabling reliable motor operation across a broader range of speeds and under sustained high-load conditions. The findings highlight the effectiveness of hybrid cooling systems in optimizing both thermal management and operational performance of electric machines. 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

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 study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-based models within MHE towards data-driven techniques. Specifically, a Long Short-Term Memory (LSTM)-based DNN is trained using synthetic data derived from a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), applied within a thermal derating torque control strategy for battery electric vehicles. The trained DNN is directly embedded within an MHE formulation, forming a discrete-time nonlinear optimal control problem (OCP) solved via the acados optimization framework. Model-in-the-Loop simulations demonstrate accurate temperature estimation even under noisy sensor conditions and simulated sensor failures. Real-time implementation on embedded hardware confirms practical feasibility, achieving computational performance exceeding real-time requirements threefold. By integrating the learned LSTM-based dynamics directly into MHE, this work achieves state estimation accuracy, robustness, and adaptability while reducing modeling efforts and complexity. Overall, the results highlight the effectiveness of combining model-based and data-driven methods in safety-critical automotive control systems. Full article