## 3 Initial Design Parameters of HSPMM
### Multi-Objective Optimization of Electromagnetic Performance and Losses
According to the Pareto front surface of the multi-objective optimization design in the important parameter subspace X1 of the rotor, three non-inferior optimal solutions are selected as candidate values of the final optimization result, as shown in Figure 5:0 The candidate values and the key motor performance parameters corresponding to the initial design are shown in Figure 5:1  
According to the multi-objective optimization results of rotor important parameter subspace, it can be known that the thermal load of the motor is relatively large, although the constraint conditions of Formula (1) are basically satisfied, but it is not ideal. According \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Back EMF (V)** & **Torque (Nm)** & **PM Loss (W)** & **Efficiency (\%)** \\ \hline Initial design & 364 & 19.1 & 31.2 & 96.1 \\ Candidate 1 & 370 & 19.6 & 7.5 & 97.4 \\ Candidate 2 & 372 & 19.7 & 8.1 & 97.4 \\ Candidate 3 & 371 & 19.6 & 7.1 & 97.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key performance by the optimized X1. Figure 10: The Pareto solutions of rotor parameters subspace. (**a**) The Pareto solutions of efficiency, PM eddy current loss and motor thermal load. (**b**) The Pareto solutions of efficiency, torque and motor thermal load. (**c**) The Pareto solutions of torque, PM eddy current loss and motor thermal load. (**d**) The Pareto solutions of torque, PM eddy current loss and efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Initial Design** & **Candidate 1** & **Candidate 2** & **Candidate 3** \\ \hline Rotor outer diameter (mm) & 88.6 & 93 & 93.5 & 92.5 \\ Air gap length (mm) & 2 & 2.5 & 2.5 & 2.5 \\ Sleeve thickness (mm) & 5 & 6 & 6.5 & 6.5 \\ PM thickness (mm) & 8 & 12.5 & 13 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal solutions of rotor. to the calculation formula and fundamental principle of motor thermal load, it is mainly related to stator important parameters, which is consistent with the results of parameter sensitivity analysis. Therefore, it is necessary to further multi-objective optimization of important parameter subspaces for stators. The main purpose is to further reduce the thermal load of the motor while ensuring that the other optimization objectives are basically unchanged, to reduce the difficulty of heat dissipation of the motor and to ensure that the temperature rise check of the subsequent motor can meet the design requirements. It can be seen from the three candidate values of rotor important parameter subspace X1 that the performance of the three candidate values is basically the same, while in the optimization of stator important parameter subspace X2, the main objective is to reduce the thermal load of the motor, and the performance of the other optimization objectives can be predicted to have a certain decline. During the rotor parameter design of high speed permanent magnet motor, the thickness of sleeve should be left a certain margin to ensure the safe and stable operation of the motor under special circumstances. Among the three candidate values, candidate value 3 has an ideal sleeve thickness, while meeting the design requirements, leaving a larger margin, and candidate value 3 corresponds to the minimum eddy current loss of the motor performance. Therefore, candidate value 3 is selected as the optimization scheme of the important rotor parameter subspace X1, and input the stator important parameter subspace X2 to carry out subsequent stator important parameter subspace multi-objective optimization. Under the condition that the value of the optimization candidate solution of the important parameter subspace X1 of the rotor is kept unchanged, the multi-objective genetic algorithm is carried out to optimize the important parameter subspace X2 of the stator, and the thermal load of the motor is further reduced. The resulting Pareto front is shown in Figure 11 below.