Electromagnetic devices containing permanent magnet (PM) are common and typical electromagnetic devices that have advantages of low-power consumption, small size, high sensitivity, and its optimization problem has aroused widespread concern. Calculation accuracy of electromagnetic properties and efficiency of optimization processes are two important factors that affect the optimization effect of electromagnetic devices. A fast calculation method was proposed in this paper that aimed at electromagnetic devices with PM characteristics of strong non-linearity and low solving precision. The response surface method was used to derive the basis function, and the error between actual measured values and calculated values was modified by the Kriging method. On the basis of the calculation model, the distributed collaborative strategy was adopted to fulfill the parallel execution of the fast computation. Simultaneously, the updating strategy of particles was improved, and the robustness of the multi-objective particle swarm optimization algorithm in solving the optimization problem of electromagnetic devices with PM was enhanced. Effectiveness of the method proposed in this paper was verified by the case study of a specific type of electromagnetic relay with PM.

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (mosfets) are becoming increasingly prevalent in various power electronic applications. However, their widespread adoption is hindered by significant reliability issues related to the gate oxide. The threshold voltage drift under alternating gate bias, commonly referred to as gate switching instability (GSI), presents a substantial challenge to reliability. Given the widespread use of SiC mosfets in power converters, researching GSI is of practical significance compared to conventional bias temperature instability. This study systematically investigated the dependence of 1700 V planar-gate SiC mosfets on factors, such as gate bias, temperature, and switching time, and also provided the form of acceleration factor based on the physical explanation. Based on this, an accelerated degradation model was developed to quantify the impact of stresses on GSI for the first time. This research enhances the understanding of GSI and establishes a foundational framework for modeling and predicting the degradation of SiC mosfets under GSI.

There are various challenges in designing a life-cycle robust permanent magnet actuator to satisfy the requirements of quality consistency. In particular, ubiquitous uncertainties from manufacturing and operating process that cause unstable or even infeasible solutions significantly increase the difficulty of the life-cycle robust design of PMA. Conventional methods usually treated these uncertainties as fixed design constraints, which neglect the nonlinear characteristic of time-dependent degradations, cannot reflect authentic impacts on the life-cycle robustness of PMA. To address this issue, this study proposed a method of life-cycle robust design optimization for PMA. An innovative time-dependent global sensitivity analysis-based classification method is first proposed to divide design variables into three categories: nonlinear, degradation insensitive, and degradation sensitive variables. Then, a modified Spatiotemporal Kriging model with Karhunen-Loeve expansion is applied to establish the life-cycle robustness model of PMA with consideration of uncertainties. This model can valid describe the nonlinear fluctuation of the life-cycle robustness. Furthermore, basis on the three categories of design variables and life-cycle robustness model, a multi-stage dynamic robust design optimization strategy is derived, which realizes the synchronous control of life-cycle robustness deterioration rate and fluctuation. Finally, taking a case of rotary PMA, the effectiveness of the proposed method is verified.

Ubiquitous uncertainty from design, fabrication, and assembly often leads to erratic or infeasible cogging torque solutions, which observably enlarges the difficulty of the robustness-oriented design optimization (RDO) of PMSM. Robustness-oriented design optimization approaches based on FEA (finite element analysis) are still inefficient. Despite adequate surrogate and analysis models, RDO's efficiency and accuracy, considering multi-source uncertainty and repetitive elements, still need to be improved to meet practical needs. Therefore, a physically based surrogate model-assisted RDO approach is proposed in this study and comprises two main contributions. First, we develop an accurate and efficient physical-based surrogate model and the data-based layer model, including an improved vector superposition approach that characterizes both the relationship between rotor permanent magnet uncertainties and the additional harmonics, and the relationship between the rotor permanent magnet design parameters and the inherent harmonics of the cogging torque. The organic combination of these two models realizes the high-efficiency and high-precision calculation of the PMSM cogging torque. Second, we propose an overall RDO strategy for the various uncertainties and high dimensionality. This study presents and discusses the RDO results of the PMSM used in high-precision robots and machine tools. Experimental and simulation results are presented to validate the effectiveness of the proposed surrogate model, strategy, and approach.