To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

No abstract

Recently, a large amount of research on deep learning has been conducted. Related studies have also begun to apply deep learning techniques to the field of electric machines, but such studies have been limited to the field of fault diagnosis. In this study, the shape optimization of a permanent magnet synchronous motor (PMSM) for electric vehicles (EVs) was conducted using a multi-layer perceptron (MLP), which is a type of deep learning model. The target specifications were determined by referring to Renault’s Twizy, which is a small EV. The average torque and total harmonic distortion of the back electromotive force were used for the multi-objective functions, and the efficiency and torque ripple were chosen as constraints. To satisfy the multi-objective functions and constraints, the angle between the V-shaped permanent magnets and the rib thickness of the rotor were selected as design variables. To improve the accuracy of the design, the design of experiments was conducted using finite element analysis, and a parametric study was conducted through analysis of means. To verify the effectiveness of the MLP, metamodels was generated using both the MLP and a conventional Kriging model, and the optimal design was determined using the hybrid metaheuristic algorithm. To verify the structural stability of the optimized model, mechanical stress analysis was conducted. Moreover, because this is an optimal design problem with multi-objective functions, the changes in the optimal design results were examined as a function of the changes in the weighting. The optimal design results showed that the MLP technique achieved better predictive performance than the conventional Kriging model and is useful for the shape optimization of PMSMs.

No abstract

No abstract