Search Results (90)

In this study, we propose a data-driven diagnostic system that uses Extreme Gradient Boosting (XGBoost) to detect, classify, and assess the severity of multiple faults in permanent magnet synchronous motors (PMSMs). The three main fault categories that are the focus of the suggested method are inter-turn short-circuit (ITSC) faults, stator open-circuit faults, and permanent magnet demagnetization. To capture fault-specific characteristics and their development with severity, discriminative electrical features are retrieved from stator currents, flux linkage, and dq-axis values. Next, using the chosen electrical indications, an aggregated diagnostic index is created to facilitate defect diagnosis and severity quantification in a single learning process. The XGBoost-based model has been shown to produce excellent diagnostic accuracy and robust separation between various fault causes via extensive assessment. It also maintains dependable performance under previously unknown operating or fault situations. These findings show that an XGBoost-only approach offers a scalable and efficient way to monitor advanced PMSM conditions in industrial and safety-critical applications. Full article

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance electric vehicles due to their power density; however, conventional field-oriented control (FOC) relies on simplified models in which electromagnetic torque is described as a function of the quadrature current component, together with constant parameters and idealized trajectories in the $i_d-i_q$ plane, limiting adaptability and reducing efficiency and operating range under real conditions. This work introduces a flux linkage vector decomposition approach for SPMSMs, in which the permanent-magnet flux is decomposed into d- and q-axis components under core saturation and integrated into an extended field-oriented control framework. An extended FOC strategy is proposed that incorporates flux linkage vector decomposition, nonlinear magnetic saturation, cross-coupling effects, and nonlinear dependencies of electrical parameters, along with resolver angle correction and dynamic modulation index management. These enhancements modify torque and voltage trajectories by shifting the voltage-limit center and improving the definition of the MTPA, FW, and MTPV regions to better match real motor behavior, enabling performance improvements. Experimental validation on an automotive powertrain using a vehicle control unit (VCU) and precalculated lookup tables (LUTs) demonstrates improvements of up to 13.5% in low-speed torque, 13.7% in high-speed power, and efficiency gains of 4–8% across operating conditions. Full article

Rotatory electric motors provide low efficiency in the case of linear motion. The reason for this is the mechanical conversion system required to convert rotary torque to linear thrust force. In this paper, two novel linear machines i.e., a Dual-Mover Segmented Secondary Hybrid Excited Linear Flux Switching Machine (DMSSHELFSM) and Dual-Stator Segmented Secondary Hybrid Excited Linear Flux Switching Machine (DSSSHELFSM), were investigated and compared for a ropeless vertical elevator system. The novelties of these designs include both series and parallel magnetic circuits, a complementary AC coil structure, and their unequal primary tooth width. Results reveal that the DSSSHELFSM exhibits better performance with higher and more sinusoidal flux linkage, higher thrust force, and a robust mechanical structure. Secondly, the selected linear motor was optimized using a deterministic optimization approach. An average thrust force of 10kN and a thrust force ripple ratio of less than 10% were considered as performance constraints during the optimization process. Finally, full-scale no-load experimental results were obtained, and they validated the research. Full article

Existing model predictive control (MPC) schemes based on virtual voltage vectors (VVVs) for dual three-phase permanent magnet synchronous motors (DT-PMSMs) typically employ a limited set of voltage vectors, which restricts further improvement in steady-state performance. Moreover, the design of switching sequences lacks systematic consideration, focusing mainly on harmonic current suppression while neglecting practical engineering challenges associated with software-layer implementation. This paper proposes an optimized model predictive torque control (MPTC) method for DT-PMSMs using an expanded voltage vector set. First, to enhance steady-state performance, an extended control set of voltage vectors is designed, which introduces not only new directions but also two distinct voltage amplitude levels, resulting in a total of 48 voltage vectors. Second, to alleviate the significant computational burden caused by traversing the extended set for prediction, a candidate voltage vector selection table is constructed based on the sector position of the stator flux linkage and the requirements for torque and flux adjustment. This approach reduces the computational load to only 10 predictive calculations per control cycle, avoiding exhaustive traversal of the extended set. Furthermore, for all VVVs in the control set, a switching sequence combining active voltage vectors with zero vectors is designed to facilitate straightforward digital implementation. Finally, experimental results are provided to validate the effectiveness of the proposed method. Full article

This paper presents a disc-type stepper motor based on PCB technology. Aiming to provide a solution for the difficulty of torque enhancement in multi-pole PCB stepper motors under the limited wiring space of the PCB stator, a novel spiral winding configuration is proposed. Without increasing the number of PCB stator layers or the overall dimensions, an axially offset layout is employed to enlarge the coil flux-linkage area, thereby increasing the electromagnetic torque. Theoretical analysis and finite element simulation results show that the proposed winding achieves approximately 30% higher torque than conventional spiral windings. Meanwhile, to address the current fluctuation problem caused by the low-inductance characteristic resulting from the coreless PCB stator, the influence of current ripple on the microstepping drive of the stepper motor is analyzed. A series-inductor approach is adopted to suppress current fluctuation, and the optimal inductor value is selected through theoretical calculation and simulation, which effectively reduces the current ripple and significantly improves the microstepping performance. Finally, a prototype is fabricated and tested experimentally. The results indicate that the motor output torque reaches 46.4 mN·m, and the step-angle error under 16-microstep drive is within 0.25°, providing a feasible solution for the design and control of PCB stepper motors in compact spaces. Full article

Modulated twelve-voltage-vector model predictive current control (MPCC), which applies two or three voltage vectors per control period, exhibits superior steady-state performance compared to modulated six-active-voltage-vector MPCC and conventional MPCC. However, implementing modulated twelve-voltage-vector MPCC requires accurate knowledge of the permanent magnet synchronous motor drive’s inductance and permanent magnet (PM) flux linkage parameters for selecting suboptimal and optimal voltage vectors, as well as computing the duty cycles of optimal vectors. Consequently, its control performance is more sensitive to model parameter inaccuracies. To mitigate parameter sensitivity, a robust modulated twelve-voltage-vector MPCC algorithm based on a model reference adaptive system (MRAS) is proposed. The MRAS-based observer estimates the inductance and PM flux linkage parameters in real time, enhancing model accuracy. The observer is designed with a stability analysis framework, where the proportional and integral gains of the MRAS are theoretically derived to ensure precise parameter estimation. The effectiveness of the proposed algorithm is validated through simulation results, demonstrating satisfactory control performance even under parameter mismatches. Specifically, the torque ripple is reduced from 1.1 A to 0.6 A, corresponding to a reduction of 45.5%. Similarly, the stator flux ripple decreases from 1.75 A to 1 A (42.9% reduction), while the total harmonic distortion (THD) is reduced from 8.39% to 5.48%, representing a 34.7% improvement. Full article

The predictive current control for the permanent magnet synchronous machine (PMSM) shows great potential in applications like flywheel energy storage, owing to its fast dynamic response and simple structure. However, under low carrier ratio conditions, conventional deadbeat predictive current control (DPCC) exhibits drawbacks such as significant current prediction error, inaccurate instruction voltage calculation, and severe torque and flux linkage coupling. This paper proposes an improved DPCC method suitable for both high and low carrier ratio operation of the PMSM. First, a modified stator voltage equation is established considering rotor flux orientation error. By treating the dq-coordinates as stationary and accounting for rotor rotation within the control period, a dynamic PMSM model is developed, effectively suppressing cross-axis coupling under low carrier ratios. Simultaneously, a multi-coordinate variable synchronization method is also introduced to eliminate prediction and voltage errors caused by cross-coordinate computation, enabling precise deadbeat control across all carrier ratios. The experimental results demonstrate that the proposed method enhances torque-flux decoupling, improves current prediction and tracking accuracy at low carrier ratios, and offers a reliable solution for dynamic control in flywheel energy storage systems. Full article

Brushless DC (BLDC) motors are increasingly used in industrial applications due to their high efficiency, reliability, and low weight. However, their performance strongly depends on the electromagnetic properties of stator and rotor core materials. This study evaluates six BLDC motor configurations, employing materials such as M19 electrical steel, 1010 low-carbon steel, magnetic PLA, and ABS, and analyzes their impact using FEMM 4.2 finite element simulations. Key electromagnetic characteristics—including flux linkage, Back-EMF, torque, and torque ripple—were compared across configurations. The reference motor with M19 steel stator and 1010 steel rotor achieved ~7 mWb flux linkage, ~39 V pk–pk Back-EMF, and 1.44 Nm torque with ~49% ripple, confirming the suitability of laminated steels for high-power-density designs. Substituting M19 with 1010 steel in the stator reduced torque by less than 10%, indicating material interchangeability with minimal performance loss. By contrast, polymer-based designs exhibited drastic degradation: magnetic PLA yielded only 3.5% of the baseline torque with sixfold ripple increase, while ABS delivered nearly zero torque and >700% ripple. Hybrid configurations improved PLA-based results by 15–20%, though they remained far below ferromagnetic cores. Overall, results demonstrate a nearly linear relationship between material permeability and both flux linkage and Back-EMF, alongside a sharp rise in torque ripple at low permeability. The findings highlight the advantages of ferromagnetic and laminated steel cores for efficiency and stability, while polymer and hybrid cores are limited to lightweight demonstrator applications. Full article

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

This paper introduces a novel sensorless speed control strategy for squirrel-cage induction motors, which ensures robust operation in the presence of external disturbances by applying the state feedback technique. Based on the induction motor model, the speed controller is synthesized by defining a sliding variable that is driven to zero through the supertwisting control law, ensuring the stabilization of the tracking error. The time derivative of the error variable is estimated using a robust differentiator based on the sliding-mode twisting algorithm, thereby eliminating the need to estimate the load torque. A robust observer is employed to estimate the rotor speed and flux linkages simultaneously. The convergence of the estimated rotor flux linkages is enforced through a discontinuous first-order sliding-mode input, while the convergence of the rotor speed estimate is attained via a quasi-continuous super-twisting sliding-mode input. In the proposed model, the inductance parameters are determined from the magnetizing inductance and the leakage inductances of the stator and rotor. A procedure is also presented for adjusting the stator resistance and leakage inductances, taking into account the squirrel-cage rotor type and the skin effect in alternating current conduction. The performance of the sensorless speed control system under variations in load torque and reference speed is validated through experimental testing. The rotor speed estimation provided by the robust observer is accurate. The reference speed tracking control, evaluated using a 1600–1700 rpm pulse train phase-shifted by 4 s with respect to a 0–0.5 N·m pulse train, demonstrates high precision. Full article

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems. Full article

The magnetic properties of electrical steel sheets, crucial for efficient electrical machine performance, deteriorate through manufacturing processes. This study investigates the impact of different manufacturing steps on magnetization behavior and specific core losses in M270-50A electrical steel, and their influence on the performance of a reluctance synchronous machine (RSM). Magnetic measurements were conducted on three material states: laser-cut strips, assembled stator cores, and press-fitted stator cores. These were integrated into finite element analysis (FEA) models, including an extended two-region stator model that separates yoke and tooth regions to reflect different manufacturing effects. Simulations examined torque characteristics and flux linkage under various loading conditions and were validated using a prototype machine. The findings of magnetic measurements indicate that manufacturing-induced stresses significantly increase magnetization demand and core losses—up to 650% and 53%, respectively. These effects lead to a 4.2% reduction in maximum air gap torque and notable changes in torque characteristic curves and d-axis flux linkage maps. Including realistic magnetic data yielded torque predictions closely aligned with experimental results and reduced discrepancy in core loss simulation by more than 50%. The study’s findings indicate that accounting for manufacturing influences in material characterization enhances modeling accuracy and enables optimized electrical machine designs and control strategies. 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

Numerous voltage vectors exist in a neutral-point-clamped (NPC) three-level inverter. Traditional three-level model predictive control incurs a heavy online computational burden. This paper proposes a model predictive torque control strategy for NPC three-level inverters with permanent magnet synchronous motor systems. First, the relationship among the stator flux linkage vector position, the torque–flux linkage increment, and the stator flux linkage variation is analyzed. Then, the candidate voltage vector sector is determined, and the candidate voltage vectors are selected from it. Meanwhile, the direction of the load current flowing to the neutral point and the voltage difference between the upper and lower capacitors are evaluated. As a result, redundant small vectors are effectively selected, reducing the number of candidate voltage vectors to six and avoiding the computation of all possible vectors. The experimental results from an NPC three-level inverter–permanent magnet synchronous motor system verify that this strategy significantly reduces the computational complexity and provides excellent dynamic and steady-state performance. Full article

The exact identification of the parameters of Permanent Magnet Synchronous Motors (PMSMs) is extremely significant to reach servo system’s excellent performance control. So as to solve the problems of slow PMSM parameter identification using the spider monkey algorithm, and easily falling into local optimal and having unstable identification results; the LSOSMO algorithm is put forward in this article, which combines logistic–sine chaotic mapping strategy, dynamic probability adaptive t-distribution method, and an opposition-based learning strategy to determine PMSMs’ electric parameters (stator resistance R_s, dq-axis inductance L_d, L_q, and flux linkage ${\psi }_f$). First, the logistic sinusoidal chaotic mapping strategy was used to enhance the uniformity of the initial population of the spider monkey optimization (SMO) algorithm. Then, in the local leader stage and the local leader decision stage of the SMO, the dynamic probability adaptive T-distribution method and opposition-based learning strategy are used to replace the greedy selection strategy, increase the position disturbance, and balance the global search and local search ability of the algorithm, so as to improve the performance and convergence speed of the algorithm. The simulation results prove that, compared to the other five algorithms’ identification results, the four parameters that are identified by the LSOSMO algorithm exhibit higher stability and accuracy, with errors that are relative to the true values remaining below 1.1%. The effectiveness and reliability of the identification algorithm is further verified by this. Full article