Search Results (767)

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 introduces a paradigm shift in permanent magnet synchronous motor (PMSM) control through the development of hybrid dual fractional-order sliding mode control (HDFOSMC) architecture integrated with evolutionary parameter learning (EPL). Conventional PMSM control frameworks face critical limitations in ultra-precision applications due to their inability to reconcile dynamic agility with steady-state precision under time-varying parameters and compound disturbances. The proposed HDFOSMC framework addresses these challenges via two synergistic innovations: (1) a dual fractional-order sliding manifold that fuses the rapid transient response of non-integer-order differentiation with the small steady-state error capability of dual-integral compensation, and (2) an EPL mechanism enabling real-time adaptation to thermal drift, load mutations, and unmodeled nonlinearities. Validation can be obtained through the comparison of the results on PMSM testbenches, which demonstrate superior performance over traditional fractional-order sliding mode control (FOSMC). By integrating fractional-order theory, sliding mode control theory, and parameter self-tuning theory, this study proposes a novel control framework for PMSM. The developed system achieves high-precision performance under extreme operational uncertainties through this innovative theoretical synthesis and comparative results. 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

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. 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

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. 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

Model predictive control (MPC) has become a popular method in motor control due to its high adaptability to multivariate control. However, one issue for this control system is constructing a reasonable cost function (CF) and obtaining appropriate weighting factors (WFs) within it. This paper addresses the issue of effectively reducing torque ripple and current harmonic content in permanent magnet synchronous motors (PMSM). Within the three-vector model predictive current control (TV-MPCC) strategy for PMSM, a new CF including current error and switching frequency terms is constructed. Combined with the technique for order preference by similarity to ideal solution (TOPSIS), the optimal control vector is obtained. Compared with traditional methods, this method reduces the complexity of adjusting WFs in the CF. Simulation results show that the motor’s torque ripple and current harmonic content are effectively reduced. Both the steady state and dynamic performance of the PMSM are also improved by means of the proposed multi-objective MPC for current error and switching frequency. Full article

In recent years, distributed energy power supply systems have been widely used in remote areas and extreme environments. However, the intermittent and uncertain output power may cause power grid fluctuations, leading to higher harmonics in electromechanical equipment, especially motors. For permanent magnet synchronous motor (PMSM) systems, an electromagnetic (EM) vibration can cause problems such as energy loss and mechanical wear. Therefore, it is necessary to design control algorithms that can effectively suppress EM vibration. To this end, a vibration suppression algorithm for fractional-slot permanent magnet synchronous motors based on a d-axis current injection is proposed in this paper. Firstly, this paper analyzes the radial electromagnetic force of the fractional-slot PMSM to identify the main source of EM vibration in fractional-slot PMSMs. Based on this, the intrinsic relationship between the EM vibration of fractional-slot PMSMs and the d-axis and q-axis currents is explored, and a method for calculating the d-axis current to suppress the vibration is proposed. Experimental verification shows that the proposed algorithm can effectively suppress EM vibration. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

Electric mountain bikes (eMTBs) demand compact, high-torque motors capable of handling steep terrain and variable load conditions. Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in this application due to their simple construction, ease of manufacturing, and cost-effectiveness. However, SPMSMs inherently lack reluctance torque, limiting their torque density and performance at high speeds. While interior PMSMs (IPMSMs) can overcome this limitation via reluctance torque, they require complex rotor machining and may compromise mechanical robustness. This paper proposes a surface-inset PMSM topology as a compromise between both approaches—introducing reluctance torque while maintaining a structurally simple rotor. The proposed motor features inset magnets shaped with a tapered outer profile, allowing them to remain flush with the rotor surface. This geometric configuration eliminates the need for a retaining sleeve during high-speed operation while also enabling saliency-based torque contribution. A baseline SPMSM design is first analyzed through finite element analysis (FEA) to establish reference performance. Comparative simulations show that the proposed design achieves a 20% increase in peak torque and a 33% reduction in current density. Experimental validation confirms these findings, with the fabricated prototype achieving a torque density of 30.1 kNm/m³. The results demonstrate that reluctance-assisted torque enhancement can be achieved without compromising mechanical simplicity or manufacturability. This study provides a practical pathway for improving motor performance in eMTB systems while retaining the production advantages of surface-mounted designs. The surface-inset approach offers a scalable and cost-effective solution that bridges the gap between conventional SPMSMs and more complex IPMSMs in high-demand e-mobility applications. Full article

The traditional deadbeat predictive current control (DPCC) strategies for a permanent magnet synchronous motor (PMSM), such as those based on an extended state observer (ESO) and quasi-resonant extended state observer (QRESO), usually require large observer bandwidth, rendering the system sensitive to noise. To address this issue, this paper proposes a cascaded quasi-resonant extended state observer-based DPCC (CQRESO-based DPCC) strategy. Specifically, the CQRESO is utilized to estimate the predicted values of d-axis and q-axis currents, as well as the system total disturbance caused by the deterministic and uncertain factors at time instant k + 1. Subsequently, the required control command voltage at time instant k + 1 is then calculated according to the deadbeat control principle. Finally, the comparative simulation results with ESO-based DPCC and QRESO-based DPCC strategies demonstrate that the proposed strategy can achieve dynamic and robust performance comparable to the ESO-based and QRESO-based DPCC strategies while utilizing a smaller observer bandwidth. Additionally, it exhibits superior steady-state performance and 5th and 7th harmonic current suppression capabilities (in the abc reference frame). Full article

This research paper presents a comprehensive comparison between Permanent Magnet Synchronous Motors (PMSMs) and Switched Reluctance Motors (SRMs) in the context of drone applications. The study focuses on motors designed for an output power of 500 watts, with a torque of 0.8 Nm. Simulation results demonstrate that both motor types achieve the specified power rating, exhibiting a torque output of 0.8 Nm. In this comparative analysis, key performance parameters, efficiency, and operational characteristics of PMSM and SRM are systematically evaluated. The study addresses the unique features and challenges associated with each motor type, providing valuable insights for optimizing drone propulsion systems. Additionally, the influence of these motor choices on drone efficiency, weight, and overall performance is discussed. The research contributes to the understanding of motor selection in drone design, offering practical guidance for engineers and researchers involved in unmanned aerial vehicle development. As drone applications continue to diversify, this comparative study aids in making informed decisions regarding motor technologies, balancing power requirements, and maximizing operational efficiency. Full article

Traditional permanent-magnet synchronous motor (PMSM) models assume constant inductance parameters in the dq frame, attributing torque ripple solely to local non-sinusoidal disturbances while neglecting nonlinear effects like iron saturation, flux linkage spatial harmonics, and inter-axis mutual coupling. These simplifications limit such models to predicting average torque but fail to capture harmonic components. To overcome these limitations, this study develops a nonlinear PMSM model using differential inductance theory and constructs a physics-informed neural network (PINN) surrogate trained on finite-element data. The proposed hybrid framework demonstrates high-fidelity torque prediction, validated against finite-element simulations, and provides insights into harmonic generation mechanisms under saturation and spatial field distortions. Full article