Abstract：The coupling between the winding and the permanent magnet in the stator slot of the five-phase stator slotted permanent magnet hybrid excitation doubly salient machine puts forward high requirements for optimization design. The multi-objective optimization process produces deformed solutions easily, and the optimization results are not comprehensive. Therefore, a five-phase 20/18 pole topology is proposed. According to the limited stator slot area of the machine structure, the no-load and power characteristics are analyzed after the slot area is rationally allocated under the DC saturation state. The slot filling rate and winding current density are taken as constraint conditions to determine the initial optimization parameters and objectives. Multi-objective layered and phased optimization with six parameters and four objectives is carried out. Combined with the orthogonal experimental design method and comprehensive sensitivity analysis, the structure parameters of the machine body are layered, and the low-sensitivity parameters are determined by the single-parameter scanning method. A parameter database of the high-sensitivity layer is established by finite element analysis, and radial basis function neural network modeling is conducted. Secondly, the improved non-dominated sorting genetic algorithm II is used to optimize the model by phases. The best data set is introduced to generate an excellent initial population. In the early phase, the parent selection method based on linear ranking accelerates the convergence speed. In the later phase, the mean value constraint is applied to the merged populations according to the average value of the database to avoid deformed solutions. The optimal design scheme is obtained according to the optimized multi-objective weighting function. Finally, the experiments verify the feasibility and effectiveness of the proposed optimization method. Using the proposed optimization method, the electromagnetic performance of the optimized machine is superior. The output power of the machine is 3649W, increased by 9.2%; the armature copper loss is 186 W, decreased by 24.3%; the output voltage pulsation is 0.0994, decreased by 24.3%; the output torque pulsation is 0.2639, decreased by 13.3%. The following conclusions can be drawn. (1) The optimization problem with six parameters and four objectives is reduced to two parameters/four objectives and four parameters/four objectives to layer the structure parameters of the machine body. The influence of fixed dimensions is eliminated, and the overall optimization workload is reduced. (2) The proposed simulation database modeling and intelligent algorithm only need 625 times of finite element simulation, while the traditional finite element optimization method requires 3652,813 times to achieve the same accuracy. In contrast, the proposed method reduces the number of finite element simulations and accelerates the machine optimization speed. (3) The improved algorithm optimizes the mathematical model of the machine by phases and removes deformed solutions in the later phase of optimization. The machine structure can balance output performance, which is suitable for the multi-objective optimization of the machine.
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2024, Vol. 39 
