Series-end winding motor drives (SWMDs) are more well known for the characteristics of high dc-link voltage utilization and controllable zero-sequence loop. Besides, SWMDs also have the potential ability to reduce the leg-current stress (LCS) by rearranging the winding-connection sequence (WCS), especially in multiphase SWMDs (MP-SWMDs). Yet, this characteristic is not widely noticed in the existing works. To this end, this article presents the analysis of WCS in MP-SWMDs for LCS reduction, which provides the possibility to improve the load capability and reliability compared with the existing WCS. Based on the phasor diagrams of the leg- and phase-currents, the principle of the WCS in MP-SWMDs for LCS reduction is revealed, which is discussed separately for the symmetrical and asymmetrical (multiple three-phase) phase-windings. By rearranging the WCS and reversing part of windings in MP-SWMDs, optimal WCSs for LCS reduction are derived accordingly. Subsequently, the corresponding modulation schemes based on the relative potential are developed. Moreover, the linear modulation ranges of different WCSs in MP-SWMDs are also investigated. With the verification of the experimental results, the current amplitude of middle legs in MP-SWMDs under the optimal WCS for LCS reduction can be reduced significantly compared with the star-connected winding counterparts. Simultaneously, the linear modulation range is also reduced, which indicates that MP-SWMDs under the optimal WCS for LCS reduction are ideal for low- or medium-speed motor drives with heavy load.

Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. To open the black-box of these deep learning models, post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trained GNN. In this paper, we show analytically that for a large class of explanation tasks, conventional approaches, which are based on the principle of graph information bottleneck (GIB), admit trivial solutions that do not align with the notion of explainability. Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions. We further introduce a novel factorized explanation model with theoretical performance guarantees. The modified GIB is used to analyze the structural properties of the proposed factorized explainer. We conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our proposed factorized explainer.

Electric vehicles (EVs) can act as virtual energy routers (VERs) in the grid, giving them the flexibility to change the direction of energy flow. Therefore, a day-ahead scheduling for radial microgrids deploying EV-based VERs is proposed. In the day-ahead scheduling, the charging/discharging operation, state of charge (SOC), and available time of EV-based VERs are involved in the social utility maximization problem. With the laxity model of EV-based VERs and the forecasted reference demand, the supply and demand in the microgrid are optimized to minimize generation costs and maximize consumer utility in a whole day. Binary variables, which indicate the charging and discharging choices of the EV-based VERs, exist in the day-ahead scheduling. Therefore, mixed integer nonlinear programming (MINLP) is adopted to solve the optimization problem. The simulation cases are then provided to verify the effectiveness of the suggested day-ahead scheduling approach.

Axial-flux permanent magnet (AFPM) machines are welcomed widely in many applications thanks to the high torque density and compactness. In the AFPM machine, the concentrated winding and distributed winding have different characteristics. In order to increase the winding factor and reduce the winding ends, a novel concentric winding AFPM machine is proposed in the paper. The stator of the proposed machine is divided into three circles from the inner side to the outer side. Then, the three-phase windings are arranged on the three circles, respectively. The phase angle difference is achieved through the mechanical offset between the teeth of three circles. Thus, the winding factor can be kept as 1 with a few winding ends by adjusting the pole-slot combination. In addition, the staggered teeth between the three circles can also reduce the cogging torque. In the optimization, the back EMFs of the three phases are nearly balanced. 3D finite element analysis also shows that the machine has a strong overload capability.

The application of wireless power transfer (WPT) in electric vehicles (EVs) has brought great convenience, safety, and flexibility to EV owners. Traditional EV charging converters can only support wired charging or wireless charging with low integration. In order to realize both wired and wireless charging functions in the same system, excessive power switches will be utilized, resulting in a redundant structure and low power density. To solve this problem, this paper proposes a novel multi-functional converter for EV charging. Using one set of power switches, the proposed converter can output DC voltage for wired charging or high-frequency AC voltage for wireless charging. Circuit topology and control method are discussed and analyzed. Finally, simulations in MATLAB/SIMULINK are conducted to verify the effectiveness of the proposed multi-functional converter.

In recent years, transverse flux permanent magnet motors (TFPMMs) have had a promising application in electric propulsion systems due to their modularity, simple winding distribution, and high reliability. However, the low motor space utilization, low average output torque, and complex manufacturing and assembly constraints have limited the further development of TFPMMs. Therefore, this paper proposes a novel double-stator transverse flux reversal permanent magnet machine (DS-TFRPMM). This new motor has no permanent magnets on the rotor and can achieve higher operating speeds. In addition, the permanent magnets are on the outer stator to improve motor space utilization. Firstly, this paper describes the structure and operating principle of the DS-TFRPMM. A 3D model of the DS-TFRPMM is then constructed, and a 3D-finite element analysis (FEA) is carried out to obtain the magnetic field distribution and the critical performance data. The simulation results show that the proposed motor has the average output torque of 11.70Nm, the torque density of 10.9Nm/L, and the 1.19Nm peak-peak value of cogging torque. Finally, the paper optimizes some critical parameters of the DS-TFRPMM and provides the dimensions of the vital motor components for optimum output performance.

Axial flux permanent magnet (AFPM) machines are welcomed in industries due to the compactness. In order to improve the fault-tolerant capability and decrease phase voltage level, a dual three-phase dual stator AFPM machine with mechanical offset is proposed. In this paper, a theoretical analysis is conducted for the harmonics of the proposed machine. The mechanical offset mainly affects the amplitudes of harmonics. Then, a 3D finite element analysis is performed to verify the theoretical analysis. The orders of main air-gap flux density harmonics are related to the number of rotor pole pairs and teeth, but the harmonics have little influence in the back electromotive force. As for the axial force density, the orders of main harmonic components are the two times number of stator pole pairs, rotor pole pairs, and teeth. The results are consistent with the theoretical analysis and in-depth discussion.