In this paper, the cooling performance of oil-cooling PMSM with hairpin winding under various oil parameters is analyzed via a simulation and an experiment. The effects of oil jet positions, oil temperatures, and oil flow rates on the cooling performance are analyzed. It is found that increasing the oil temperature in the range of 20 °C to 60 °C, increasing the flow rate of oil jets whose position angle is from 15° to 45°, and increasing the flow rate in the range of 1 L/min to 2 L/min will significantly improve the cooling performance. The apertures of the oil spray ring are optimized using the Taguchi algorithm. The cooling performance is the best when the flow ratio is m(0°):m(15°):m(30°):m(45°):m(60°):m(75°) = 4%:19%:10%:10%:4%:4%. This study provides a guide for the design of the oil-cooling system for the hairpin winding of the PMSM.

Accurately assessing the remaining useful life (RUL) is a significant challenge to the reliability of Silicon Carbide (SiC) MOSFETs and is crucial for their safe operation. Consequently, this paper proposes a novel data-driven prediction method that integrates Successive Variational Mode Decomposition (SVMD), the Sparrow Search Algorithm (SSA), and the Transformer model. The threshold voltage V_th is selected as the degradation parameter for prediction. Firstly, SVMD is utilized to decompose the original V_th data into a degradation trend component and several fluctuation components with different central frequencies, thereby providing a more precise feature for prediction models. Subsequently, based on the Transformer model, trend predictions are conducted on each intrinsic mode function (IMF) derived from SVMD, and these results are aggregated as the final predicted value of V_th. The hyperparameters of the Transformer are optimized using SSA to enhance prediction accuracy. Ultimately, a power cycling platform is constructed to acquire the dataset of the device, where the device is subjected to rated current and 80 °C junction temperature fluctuation stress during testing. Building upon this, the difference between the number of cycles when V_th reaches its upper limit and the current number of cycles is determined as the predicted RUL value. Results demonstrate that compared to both a single Transformer model and the SVMD-Transformer model, the proposed method achieves a higher coefficient of determination (R²) and a lower root mean square error (RMSE), indicating superior prediction performance.

This article proposes a new adaptive gain, full-order terminal sliding mode control algorithm for the speed regulation of a surface-mounted permanent magnet synchronous motor (SPMSM) control system. To deal with the mismatched uncertainties in the double-loop nonlinear system of the SPMSMs, a virtual control technique is constructed with the full-order terminal sliding mode control to ensure that the tracking error trajectory can converge to equilibrium in finite time. Owing to the integral control law, the output signals of the controllers are smoothed, with the chattering phenomenon attenuated and the gain-margin overestimation avoided. Comprehensive simulation and experimental results have been carried out to demonstrate the superiority of the proposed method in improving tracking accuracy, rapidness, and robustness to the matched and mismatched uncertainties.

Studies on the central and bilateral bridges of interior permanent magnet (IPM) motors often focus on individual mechanical strength or electromagnetic performance, lacking comparative studies on the electromagnetic performance of motors with different central and bilateral bridges under the same mechanical strength. This paper designs three rotors with different central and bilateral bridges and compares the electromagnetic performance of the three motors. First, to ensure the safe operation of the three rotors at high speeds, the mechanical stress of each rotor has been analyzed using the finite-element method (FEM). Subsequently, the major electromagnetic performances of the three motors are analyzed and compared, including the air-gap flux density, back electromotive force (back-EMF), inductance, salience, torque, power, loss, efficiency, and demagnetization. The results indicate that the rotor without central bridges has the largest leakage flux and the lowest torque but exhibits minimal torque ripple. The rotor with narrower bilateral bridges has the highest torque and maximum torque ripple. The torque performance of the rotor with wider bilateral bridges lies between the two aforementioned motors, and it possesses the highest efficiency. In the end, by adjusting the dimensions of the permanent magnets, the torque of all three models increases, but the motor with narrower bilateral bridges still has the largest torque. These findings provide valuable references for rotor design.

Stator winding reconfiguration technology has attracted increasing attention because it can modify the torque–speed output characteristics of motors online and extend the high-efficiency operating region. However, conventional winding switching methods suffer from several limitations, including long transient duration, typically at the millisecond level; difficulty in precise control; current interruption or asymmetric operation during the transient process; and the resulting torque loss and torque fluctuation. To address these issues, this paper first proposes a seamless winding switching method. The core idea is to complete the switching within the zero-vector interval of a single SVPWM period, thereby eliminating potential surge voltage without requiring a snubber circuit. The proposed method reduces the transient duration to only 5 μs, eliminates current interruption and current asymmetry, and suppresses the associated torque ripple. Furthermore, a deadbeat predictive control strategy is employed. By directly controlling the voltage vector, the proposed strategy enables fast and accurate current tracking within one sampling period after reconfiguration, thereby avoiding current overshoot, oscillation, and the corresponding torque and speed fluctuations. In addition, a voltage feedforward compensation method is proposed to pre-compensate the voltage applied during the PWM period in which the winding reconfiguration is performed, thereby preventing the application of voltage vectors that are inconsistent with the actual motor winding connection. With these three innovative measures, fast, smooth, and robust winding reconfiguration is achieved. The mechanisms and implementation procedures of the proposed strategies are described in detail, and their feasibility and effectiveness are verified through experiments.

Owing to the stringent reliability requirements of silicon carbide (SiC) MOSFETs, conventional gate-driving methods with fixed parameters are no longer sufficient to ensure reliable operation under varying operating conditions. To address this issue, this study proposes an adaptive gate-driving method based on model prediction dealing with operating conditions. First, the influence of operating conditions on crosstalk is comprehensively analyzed, starting with an explanation of the crosstalk mechanism. Then, a two-variable behavioral model is established to estimate the amplitude of the crosstalk voltage under the specified operating condition. Based on this prediction model, a lightweight optimization algorithm is developed to select the negative gate turn-off voltage according to the operating conditions. Finally, a double-pulse test platform is built to validate the proposed dynamic gate-voltage selection strategy. The experimental results show that, compared with the fixed −3 V turn-off voltage scheme, the proposed method selects the optimal turn-off voltage of −2 V under specific operating conditions, which can reduce unnecessary negative gate-voltage stress while maintaining the gate-source voltage within the allowable range. Additionally, the measured turn-off loss of the proposed method is 172.7 μJ, lower than the 222.3 μJ loss of the parallel gate-source capacitance method, indicating a better trade-off between crosstalk suppression and switching loss. The proposed behavioral model requires only 69.5 microseconds to predict positive and negative crosstalk peaks, whereas the analytical model requires 46.6 milliseconds. The low computational burden makes the proposed method suitable for potential real-time implementation on resource-constrained microcontrollers.