Permanent Motor Research Articles

Design, Analysis, and Comparison of Permanent Magnet Claw Pole Motor with Concentrated Winding and Double Stator

Permanent magnet motors have become an important component of industrial production, transportation, and aerospace due to their advantages of high torque density, high power density, high reliability, low losses, and high efficiency. Permanent magnet claw pole motor (PMCPM) is a special type of transverse flux motor which has a higher torque density compared to traditional permanent magnet motors. Due to the absence of winding ends, its axial space utilization is high, and the usage of windings is greatly reduced, reducing the cost and weight of the motor. PMCPM has the advantages of small space, a light weight, a high torque density, a high efficiency, and simple production, which have potential for use in the field of electric vehicles. The double-stator structure design can improve the torque density, efficiency, and radial space utilization of PMCPM, which helps to expand their applications in the field of electric vehicles. This article designs two PMCPM with concentrated winding while different rotor structures (PMCPM1 and PMCPM2) and a three-dimensional finite element method is employed to compare and analyze the performance of PMCPM1 and PMCPM2 and the traditional PMCPM (TPMCPM). Multiphysics analysis is carried out for PMCPM1 and PMCPM2. The stress of the inner and outer stators during interference assembly are analyzed. In this paper, a hybrid material core design is proposed, in which the stator yoke is rolled by silicon steel material and the stator claw pole is pressed by the SMC die method. The multiphysics simulation performance of the PMCPM1 and PMCPM2 with hybrid cores is analyzed.

Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor

An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This paper presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.

Comprehensive Armature Reaction Modeling and Flux Weakening Optimization of a Surface Permanent Magnet Vernier Motor

Surface permanent magnet vernier motors (SPMVMs) possess the potential for direct-drive applications due to the feature of high torque density. This paper presents a deep investigation into the armature reaction modeling and a semi-analytical optimization framework considering the constant power speed range (CPSR) in the flux weakening region for the SPMVM. The new armature reaction model can identify the armature air gap flux density of each harmonic component and armature flux linkage accurately, by conducting harmonics analysis, slot leakage flux computation and slotting effect evaluation, permitting to calculate the inductance. Then, the results from the armature reaction model and those obtained from finite element analysis (FEA) at the rated operating point together are applied to a comprehensive semi-analytical motor optimization framework developed for SPMVMs. The optimization framework is able to reflect the motor performance metrics over the entire speed range, with torque density, power factor and CPSR as objectives. Finally, a prototype designed according to the trade-offs of the optimization objectives is fabricated to confirm the validity of the armature reaction model and optimization framework.

Cold-Spray Additive Manufacturing of a Petal-Shaped Surface Permanent Magnet Traction Motor

Several applications require low torque pulsations as they can lead to mechanical vibrations and acoustic noise in the electric motor. Optimization of the rotor permanent magnet (PM) shape is one of the effective methods for reducing torque pulsations. Unfortunately, the low versatility of the magnet fabrication technologies limits the development of new motor geometries. Cold spray additive manufacturing can be used for shaping permanent magnets for the direct fabrication of motor parts without the need for additional assembly steps. This fabrication technique allows an increase in the design flexibility of electric machine geometries targeting improved performance. This paper investigates the performance of permanent magnet rotors fabricated using a cold spray additive manufacturing technique for radial flux surface permanent magnet synchronous machines (SPMSM). The permanent magnet rotors considered are conventional rectangular-shaped with unskewed magnets (Model A), skewed magnets (Model B), and sinusoidal-petal shaped magnets (Model C) along the axial direction. The magnitude of magnetization current pulse required to magnetize these rotors is calculated and an impulse magnetizer setup is designed for in-situ magnetization. The performance of the shaped cold sprayed permanent magnet rotors and their effects on back EMF, electromagnetic torque, and cogging torque are analyzed experimentally and comparisons are made between the different rotor designs.

모빌리티 적용 영구자석형 고출력 모터 설계 기술

모빌리티 적용 영구자석형 고출력 모터 설계 기술

Precision medicine for traumatic cervical spinal cord injuries: accessible and interpretable machine learning models to predict individualized in-hospital outcomes

BACKGROUND CONTEXTA traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases. PURPOSEThis study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI). STUDY DESIGNOur study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI. PATIENT SAMPLEThe data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021. OUTCOME MEASURESThe outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall. METHODSThe study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest. RESULTSThere were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics. CONCLUSIONSOur study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients.

Identification and Analysis of Noise Sources of Permanent Magnet Synchronous Traction Motor with Interior Permanent Magnet

The rapid development of electromobility is placing ever higher demands on new electric motor designs. This results in a gradual reduction in weight with a simultaneous increase in maximum torque. As a result, unfavorable phenomena, such as vibration and noise, can become apparent in the drivetrain. Modeling and evaluation of the acoustic noise sources of a traction motor are particularly important when it is used, for example, as the traction drive of an electric bus, where too high noise levels can have a negative impact on passengers. This article describes methods for analyzing and evaluating the root causes of noise that occurs in permanent magnet traction motors with a rotor in which the magnets have been placed inside the rotor (PMSM IPM). This paper presents an analysis of acoustic noise and forces acting in the air gap of a 240 kW motor with 60 stator slots and 2p = 10 (s60p20) as the number of pole pairs designed for bus and truck drives. To determine the dominant noise sources and evaluate their value, the forces acting in the air gap and their effect on the deflection of the outer surface of the stator yoke were calculated. The natural frequencies of the machine, their frequencies for the entire rotor speed range, and the frequency of vibration of the motor stator were calculated. Based on these data, the sound power level (A-SWL) was calculated at varying motor speeds. MANATEE software (EOMYS, 9, avenue de la Créativité, 59650 Villeneuve d’Ascq—FRANCE) from EOMYS was used to perform vibroacoustic calculations. The analysis results were also subjected to verification on a laboratory bench.

LDH-doped gelatin-chitosan scaffold with aligned microchannels improves anti-inflammation and neuronal regeneration with guided axonal growth for effectively recovering spinal cord injury

Spinal cord injury (SCI) causes immune cell infiltration, neuronal death and permanent motor dysfunction. Effective treatment for SCI requires simultaneous suppression of the inhibitory and inflammatory microenvironment and enhancement of the pro-regenerative potential at SCI sites. However, realizing neuronal regeneration with guided axon growth and anti-inflammation simultaneously is currently challenging. In this study, a layered double hydroxides (LDH)-doped gelatin-chitosan scaffold with aligned microchannels (GC-LDH/A scaffold) combined with chemical and structural repair effects was developed to regulate immune microenvironment and promote directional neurogenesis. The GC-LDH/A scaffold was prepared by anisotropic freezing of a mixture of Mg-Fe layered double hydroxides (Mg-Fe LDH), gelatin and chitosan. The LDH, gelatin and aligned microchannel structure work together to promote neuronal regeneration. Chitosan and the aligned microchannel structure have a function of anti-inflammation while the latter also guides axonal growth. The repair effect of this scaffold was assessed by restoring a completely transected spinal cord injury, and the results showed that the GC-LDH/A scaffold effectively activated the anti-inflammatory polarization of macrophages and microglia, promoted neurogenesis and guided axonal growth from the rostral to caudal of the lesion site. The key mediator of functional recovery in SCI is Myosin II, which negatively regulates axonal growth; however, the aligned microchannel structure inhibits its activity and reverses the repair effect. This study provides a promising strategy for addressing the repair challenges for effective SCI treatment.

Identification of Rotor Position of Permanent Magnet Spherical Motor Based on Compressed Sensing

The accurate identification of the rotor position of permanent magnet spherical motor (PMSM) is crucial to its closed-loop control. A novel method for rotor position identification of the PMSM based on compressive sensing is proposed, which can obtain accurate rotor position by a limited number of Hall sensors. Firstly, the paper designs a Hall sensor array based on the distribution characteristics of the rotor magnetic and the frequency domain characteristics of the air gap magnetic field, combined with the Fibonacci sequence. Then, an observation matrix construction method is proposed, used for compressed sensing of the radial magnetic field strength information in air gap. Finally, the position of the spherical rotor is identified according to the reconstructed information based on the sensor output value. The identification method is verified by experiments, which shows that the identification accuracy is about 1 degree.

Diagnosis of material wear on the running track bearing of a synchronous permanent magnet electric motor

This study addresses the issue of creating methods for diagnosing the condition of bearing assemblies using an onboard diagnostic complex of an urban electric bus. The purpose of the study is to identify types of rolling bearing failures in the composition of the traction electric drive of urban route transport and to develop methods for their diagnosis using analysis of phase currents in the windings of the electric motor. As a result of the study, a new mathematical model of resistance torque was developed, and the diagnostic feature value of defect development was established. Thus, this study has practical significance for ensuring the reliability and safety of urban transport, especially under conditions of intensive operation and high loads on the electric drive. Keywords: urban electric transport, diagnostics, bearing assemblies, onboard diagnostic complex, analysis of phase currents, mathematical model, defect

Abnormal changes of bone metabolism markers with age in children with cerebral palsy.

Cerebral palsy (CP) is a broad range of diseases with permanent and nonprogressive motor impairments, carrying a high cost for both the individual and the society. The characteristics of low bone mineral density and high risk of fractures suggest that bone metabolism disorders are present in CP. This study aims to investigate the association between indicators of bone metabolism and children with CP. A total of 139 children (75 children with CP and 64 healthy controls) were included in this cross-sectional study. Participants were divided into three age groups (0-2 years, 2.1-4 years, and 4.1-7 years). All children with CP were diagnosed according to clinical criteria and furtherly divided into clinical subtypes. The levels of total procollagen type I N-terminal propeptide (TPINP), N-MID osteocalcin (OC), beta-crosslaps (β-CTX), 25-hydroxyvitamin D (25-OHD) and parathyroid hormone (PTH) in the serum were measured with corresponding detection kits according to the manufacturer's instructions. Serum levels of TPINP and 25-OHD were lower with older age, whereas β-CTX and PTH were higher with older age. In the CP group, TPINP (age 0-2 years and 2.1-4 years) and OC (age 2.1-4 years) levels were higher, while β-CTX (age 2.1-4 years and 4.1-7 years) and PTH (age 2.1-4 years) values were lower than the control group. In addition, there were no statistically significant differences in the levels of these indicators among the CP subgroups with different clinical characteristics. Our study shows that bone turnover markers, indicators of bone metabolism, in children with CP differ significantly from healthy controls. The indicators we studied changed with age, and they did not correlate with disease severity.

A Review on Analysis Methods and Research Status of Hysteresis Motor

A hysteresis motor produces output torque through the hysteresis effect of magnetic materials. It has the advantages of a simple structure, high-speed operation, high temperature resistance, low noise and self-starting capability. It can be applied to some special occasions requiring high speed and high stationarity. However, its disadvantage is low torque density, low efficiency and low power factor. The permanent magnet hysteresis motor is a compromise of the characteristics of permanent magnet motor and hysteresis motor, and it can be self-starting in the case of having a torque density comparable to that of a permanent magnet motor. In addition, there are some new structures of hysteresis motors, which open up the direction for innovative applications. Due to the complexity of magnetic properties, the calculation methods and dynamic models of hysteresis motors and permanent magnet hysteresis motors are special and also depend on the research of hysteresis materials and hysteresis models. This paper starts from the principle and classification of the hysteresis motor, and different structures and the corresponding analysis methods are reviewed. The motors with new structures and new methods are emphasized, the innovation and contribution of existing research are summarized, and the development trend of hysteresis motors is described.

Electromagnetic analysis and detent force minimization of a linear vernier motor

This paper proposes a new dual-sided linear permanent magnet vernier motor (LPMVM). The advantages of high thrust density, low thrust ripple, positive force elimination and structural stability are introduced in detail. The double-sided structure and toroidal winding proved the novelty of the machine tool, which is suitable for high definition digital control machine tools. Firstly, the basic structure and working principle of the engine are introduced. Through theoretical analysis, the expressions of electromagnetic thrust and detent force are derived. Thirdly, basic characteristics of permanent magnetic magnet flux coupling, backless electromotive force, detent force and electromagnetic drive are analyzed by using a a simplified three-phase model with symmetric boundary in 3-D finite element analysis. Finally, the thrust density and the detent force of the designed motor are calculated and analyzed. The results show that the designed motor is superior to the linear permanent magnet synchronous motor.

Impact of multimodal intraoperative neurophysiological monitoring of the spinal cord during spine and spinal cord surgeries

BackgroundThere is growing evidence of the valuable role of multimodal intraoperative neurophysiological monitoring (IONM) during spine and spinal cord surgeries, as it provides an opportunity to recognize functional changes of the neural elements, usually in the reversible stage, during complex surgical manipulations. Therefore, it may reduce the risk of postoperative neurological dysfunction and improve functional outcomes. The aim of the current study was to evaluate the efficacy of multimodal IONM for preventing and predicting iatrogenic neurological dysfunction during spinal cord and spine surgeries.ResultsSignificant alerts had occurred in 9 out of 24 patients; however, all the changes were reversible and did not contribute to postoperative clinical deterioration. Only one case got worsened in the early postoperative follow-up period, with stable intraoperative monitoring.ConclusionIntraoperative monitoring is a valuable tool in spine and spinal cord surgeries. Combined transcranial motor-evoked potential (TcMEP), somatosensory-evoked potential (SSEP), and spontaneous electromyography (EMG) monitoring may prevent permanent motor deficit and enhance the postoperative outcomes.

Intelligent Backstepping Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.

Design and Optimization of PMSM for Injection Molding Machine

With the implementation of the “dual carbon policy”, the traditional injection molding machine transmission system pays more and more attention to improving the energy efficiency for the drive motor, and the PM motor has obvious advantages in injection molding machine drive motor because of its high efficiency and high-power density. A permanent magnet servo motor (PMSM) for an injection molding machine with a rated power of 7 kW is designed based on the theory of PMSM in this paper. The 2D finite element analysis (FEA) model of the motor is set up. The electromagnetic performance parameters are obtained, including cogging torque, torque ripple, output torque, and no-load back EMF. The cogging torque of the motor is optimized and analyzed. The results show that the optimized motor has reduced the cogging torque and torque ripple without affecting the output capacity, which is more suitable for the application in the drive system of the injection molding machine.

Low-Speed Model Predictive Control Based on Modified Extended State Observer of Arc Motor

The article proposes a low speed model predictive control (LSMPC) based on modified extended state observer with low resolution encoder for the field modulated stator permanent magnet arc motor (FMSPMAM). The resolution of the encoder and the large torque ripples caused by the motor structure limit the expansion of the drive system to lower speeds. In order to reduce the contradiction between high bandwidth and high noise, and improve the performance of the drive system in low speed, a modified linear extended state observer (LESO) with a feedforward compensation is designed to estimate the speed and disturbance. Moreover, a dead time compensation is equipped to reduce the voltage distortion in light load and low speed. The experimental results verify the effectiveness of the proposed method in low speed with low resolution encoder.

Comparative study of preoperative functional imaging combined with tractography for prediction of iatrogenic motor deficits.

Robust preoperative imaging can improve the extent of resection in patients with brain tumors while minimizing postoperative neurological morbidity. Both structural and functional imaging techniques can provide helpful preoperative information. A recent study found that transcranial magnetic stimulation (TMS) tractography has significant predictive value for permanent deficits. The present study directly compares the predictive value of TMS tractography and task-based functional MRI (fMRI) tractography in the same cohort of glioma patients. Clinical outcome data were collected from charts of patients with motor eloquent glioma and preoperative fMRI and TMS studies. The primary outcome was a new or worsened motor deficit present at the 3-month postoperative follow-up, which was termed a "permanent deficit." Postoperative MR images were overlaid onto preoperative plans to determine which imaging features were resected. Multiple fractional anisotropic thresholds (FATs) were screened for both TMS and fMRI tractography. The predictive value of the various thresholds was modeled using receiver operating characteristic curve analysis. Forty patients were included in this study. Six patients (15%) sustained permanent postoperative motor deficits. A significantly greater predictive value was found for TMS tractography than for fMRI tractography regardless of the FAT. Despite 35% of patients showing clinically relevant neuroplasticity captured by TMS, only 2.5% of patients showed a blood oxygen level-dependent signal displaced from the precentral gyrus. Comparing the best-performing FAT for both modalities, TMS seeded tractography showed superior predictive value across all metrics: sensitivity, specificity, positive predictive value, and negative predictive value. The results from this study indicate that the prediction of permanent deficits with TMS tractography is superior to that with fMRI tractography, possibly because TMS tractography captures clinically relevant neuroplasticity. However, future large-scale prospective studies are needed to fully illuminate the proper role of each modality in comprehensive presurgical workups for patients with motor-eloquent tumors.

Three-Vector Model Predictive Suspension Force Control for Bearingless Permanent Magnet Slice Motor

Aiming at the defects of signal detection and transmission delay leading to the reduction of control accuracy in the digital control of bearingless permanent magnet slice motor(BPMSM), a three-vector model predictive suspension force control(MPSFC) strategy is proposed. Firstly, the suspension force prediction model of the BPMSM is established and the dead-beat principle is used to calculate two active voltage vectors and a null vector duty cycle. Then, according to the principle of the minimum switching state in one control cycle, simplifying the candidate voltage vector combinations, which effectively reduces the calculation amount and inverter switching frequency. Finally, the simulation and experimental results show that the proposed three-vector MPSFC strategy of the BPMSM can not only effectively improve the control accuracy, but also implement the stable suspension of the rotor. In addition, it has good static and dynamic response characteristics.

Pretreatment with troxerutin protects/improves neurological deficits in a mouse model of traumatic brain injury

Traumatic brain injury (TBI) is the major and leading cause of mortality and an alarming public health challenge. TBI leads to permanent cognitive, motor, sensory and psychotic disabilities. Patients suffering from the various and long-term repercussions of TBI currently have limited therapy choices. The current research work was designed to evaluate the beneficial and neuroprotective role of Troxerutin (Trox) (a natural flavonoid) in a closed brain injury mouse model. The male BALB/c 8-weeks old mice (n꞊150) were randomly distributed in three experimental groups. Control group of mice (n꞊50), TBI group (n꞊50) and Trox pre-treated mice group (Trox + TBI, n꞊50). The mice in Trox + TBI were pre-treated with Trox (150 mg/kg, 7 days) before TBI. The weight-drop mechanism was used to induce mild-moderate injury in mice in both the groups. Our results showed that the mice pre-treated with troxerutin significantly improved neurological severity score, blood glucose level, food intake and brain edema as compared to the mice in the TBI group. Furthermore, compared to the TBI group, the mice treated with troxerutin improved cognitive behavior as evaluated by Open field test, Shallow Water Maze and Y-Maze, decreased brain-infarct volume and blood-brain barrier (BBB) permeability, significantly decreased Reactive Oxygen Species (ROS), improved neuronal morphology and survival in the brain regions such as cortex and hippocampus. In summary, our data provided evidence that pre-treatment with troxerutin improved neurological functions, decreased the BBB permeability, improved behavior, reduced ROS and increased neuronal survival in the weight-drop close head traumatic injury mouse model.