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In this study, an approach was proposed to interpret the shapes of electric motors obtained by topology optimization (TO). A convolutional neural network (CNN) model was trained on motor images and used to predict the degree of influence of local structures of motors on their characteristics. This prediction was obtained as heatmaps for visualizing the importance of each local structure. The proposed method was applied to a permanent magnet motor model. The proposed method facilitated successful visualization of the degree of influence on average torque, torque ripple, and mechanical stress. This was validated by comparing the obtained predictions with finite element analysis results. In addition, the proposed method allowed us to improve the motor characteristics through visualization. Therefore, this approach can improve the mechanical stress resistance while maintaining the torque characteristics.

This study presents a novel homogenized finite element (FE) method that can consider parasitic capacitances of the multi-turn coils. In this method, a total capacitance, describing the parasitic capacitances, is parallel to the homogenized region, and the resonant peak is identified only by solving the electrostatic and magnetostatic FE analysis. The proposed method is applied to the solenoid and planar coil models, and it is confirmed that the impedance characteristics including the resonant peaks can be represented. Moreover, the computing cost is shown to be significantly lower than that of the conventional full-wave FE analysis.

This article presents a strategy of optimizing inductors with non-linear properties, using Monte Carlo tree search (MCTS). Compared with the existing optimization tools, the proposed method can simultaneously optimize global configuration, such as material, number of turns and winding arrangement, and local geometry. It indicates that the strategy statistically provides a best solution from global and local aspects after iterations with different lengths of chromosomes, which is challenging in conventional optimization techniques. The covariance matrix adaptation evolution strategy (CMA-ES) is used to solve the parametric optimization. For validation, the optimizations on 2-D inductors are performed. The proposed method is very suitable for the optimization of devices with possibly different global configurations. The most notable originality of this work is in the proposal of an inherited search for design targets with different emphases, suggesting that using an inherited search from the previous search history can make it easier to find the optimal solution.

This article proposes a novel optimization method to design the transmitting and receiving coils of a wireless power transfer (WPT) device considering its magnetic and circuit properties. In the proposed optimization, the coil shapes are determined by parameter optimization (PO) for manufacturability, whereas the magnetic core shapes introduced near them are determined by topology optimization (TO). The power transfer efficiencies of the optimized WPT device are evaluated using the SPICE simulation considering the coupling with an electronic circuit. The circuit parameters of the optimized coils are computed using the homogenization-based finite element method (FEM), which can effectively evaluate the eddy current loss in the coil windings. It is shown that the optimized WPT device has a power transfer efficiency higher than 90% even when there is a misalignment.

This study proposes a novel approach using convolutional neural networks (CNNs) to predict the torque characteristics of an interior permanent magnet (IPM) motor. The proposed method allows CNNs to predict the average torque and torque amplitude under various magnet configurations using the magnetic flux density distribution of the stator core. The proposed method, compared with methods trained only on the magnetic flux density distributions of the rotor core, improves the generalization performance in predicting torque characteristics. This significantly reduces the training data. In addition, the topology optimization (TO) time using the proposed method is reduced by 45% compared with the conventional method that only utilizes the finite element method (FEM).

This article introduces a topology optimization method for a surface permanent magnet (SPM) motor with high torque density suitable for air mobility. The proposed method can be used to determine the material distribution and magnetization direction in PMs to maximize the torque density at a constant current. The torque performance of the optimized motor is superior to that of a conventional motor with a Halbach array, whose magnetization varies linearly along the circumferential direction. The novel optimized motor has a triangular magnetic material on the rotor surface and an air region inside the rotor.