Multi-Objective Optimization Strategy for Permanent Magnet Synchronous Motor Based on Combined Surrogate Model and Optimization Algorithm

Yinquan Yu (), Yue Pan, Qiping Chen, Yiming Hu, Jian Gao, Zhao Zhao, Shuangxia Niu and Shaowei Zhou
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Yinquan Yu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Yue Pan: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Qiping Chen: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Yiming Hu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Jian Gao: School of Electrical and Information Engineering, Hunan University, Changsha 410006, China
Zhao Zhao: Faculty of Electrical Engineering and Information Technology, Otto-von-Guericke University of Magdeburg, 39106 Magdeburg, Germany
Shuangxia Niu: Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Shaowei Zhou: CRRC Changchun Railway Vehicles Corporation Limited, 435 Qingyin Road, Changchun 130062, China

Energies, 2023, vol. 16, issue 4, 1-17

Abstract: When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

Keywords: IPMSM; sensitivity analysis; surrogate model; Taguchi method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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