## 1 Introduction
[18] analyzed the non-passive surrogate model, and finally used the random forest surrogate model and NSGA-II to optimize the target performance of the motor. [PERSON]. [19] proposed a strategy based on the combination of a high-precision combined surrogate model and the optimization method. In this paper, the Latin Hypercube Method (LHS) was used to sample the sample points, and then six algorithms were used to establish and compare the surrogate model. Finally, the NSGA-II and the Taguchi method were used to optimize the motor. A combination of neural network and genetic algorithm was used to optimize the topology of the motor, which effectively reduced the cogging torque [20; 21]. The combination of common response surface method and NSGA-II to optimize the design parameters could also effectively improve the torque performance of the motor [22; 23; 24]. At present, the research on low-speed high-torque permanent magnet motors is mostly the exploration of new structures, the improvement in electromagnetic performance and the exploration of new optimization methods. However, in the current literature, the research on the pole-slot ratio, which is the most important difficulty in the initial design of low-speed high-torque permanent magnet motor, is far from enough. This makes the designer unable to judge and select the appropriate pole-slot matching scheme. Under the premise of taking into account the electromagnetic performance of the low-speed high-torque motor, the reduction in torque ripple, the increase in maximum output torque and the reduction in the material cost, there is a need to select the appropriate pole-slot ratio scheme. In addition, in the optimization design, there are a variety of sample point sampling methods and surrogate model fitting methods. The accuracy of the surrogate model obtained by combining different sampling methods and surrogate model fitting methods is different. However, the existing literature mostly focuses on the exploration of new optimization algorithms, ignoring the importance of the correct establishment of the surrogate model, and the optimized results are not compared with the prototype. In summary, a comprehensive comparison of the pole-slot ratio scheme can provide a certain reference value for the design of low-speed high-torque permanent magnet motors; analysis of the influence of different sampling methods and surrogate model fitting methods on the accuracy of the surrogate model is also a solid foundation for motor optimization, which enables designers to adopt appropriate sampling methods and surrogate model fitting methods to establish a high-precision surrogate model when optimizing the motor. In this paper, based on the 37 kW,160 rpm motor, a comprehensive comparative study of the pole-slot ratio and the influence of different sampling methods and surrogate model fitting methods on the surrogate model are analyzed. In Section 2, four different types of motor models with representative pole-slot ratio schemes are established, and the electromagnetic performance of the four motors is comprehensively compared. In Section 3, the influence of the design parameters of the surface-mounted low-speed high-torque permanent magnet motor on the electromagnetic performance of the motor is analyzed, and two commonly used sample point sampling schemes (CCD, LHS) and three surrogate model fitting methods (GA, Kriging, ANN) are selected. Six kinds of surrogate models are obtained by combining them, and the influence of different sampling methods and surrogate model fitting methods on the fitting accuracy of these six surrogate models is analyzed, and the motor is optimized. In Section 4, according to the optimized results, a prototype is made to verify the finite element simulation results and the optimization results of the surrogate model. The comparison results of the pole-slot ratio scheme and the surrogate model in this paper are summarized in Section 5.