VP10-01 Hybrid Surrogate Model-Based Multidisciplinary Optimization on High Speed Permanent Magnet Synchronous Motor Facilitating Rotor Design
Creators: Yihan Liu
Fengge Zhang and Xiaoyi Luo
Publisher: name: IEEE
Year: 2025

High-speed permanent magnet synchronous motor (HSPMSM) is electromechanical energy conversion device with deep multi-physics field coupling, featuring small volume and high power density. During high-speed rotation, predicting critical speed is essential to prevent rotor bending resonance. Permanent magnet material necessitates the carbon fiber sleeve due to tensile stress limits. Multi-objective collaborative optimization is required to achieve near-limit material application. Traditional serial design of finite element models isolates dynamic constraints between various physical fields and neglects the dynamic cross-influences of nonlinear changes [1]. Surrogate model enables efficient multidisciplinary parallel optimization by predicting data from sample sets, crucial for multi-objective optimization [2]. However, multiple optimization objectives and large nonlinear search space of HSPMSM reduce model accuracy. This paper proposes an improved hybrid surrogate model composed of sub-models with the highest fitting accuracy for each variable, resulting in high global surrogate accuracy and wide range. Multidisciplinary optimization design is for a 100 kW, 20000 r/min HSPMSM, as shown in Fig.1(a) and (b). The objective variables include maximizing sleeve tangential stress and magnet radial stress within constraints, and minimizing core loss, copper loss, and torque ripple. Constraints include no-load back electromotive force and critical speed. The mathematical model is built. Sensitivity analysis using the LTSM-SOBOL model identifies six structural parameters for optimization. Optimal Latin Hypercube Sampling generates a highly space-filling sample set. Various surrogate models are trained using the sample set to establish a hybrid surrogate model, shown as Fig.1(c). NSGA-II is used for multidisciplinary optimization [3]. This approach effectively decreases the sleeve thickness and reduces losses, verified through finite element analysis, yielding a high-reliability and high-efficiency motor optimization solution, as shown in Fig.2(a) and (b). This method is applicable to multidisciplinary optimization of various types of PMSM.References: [1] G. Bramerdorfer, J. A. Tapia, J. J. Pyrhonen, et al, IEEE Transactions on Industrial Electronics., Vol. 65, p. 7672–7684 (2018) [2] B. Xie, Y. Zhang, Z. Xu, et al, IEEE Transactions on Energy Conversion., Vol. 38, p. 859–868 (2023) [3] K. Deb, A. Pratap, S. Agarwal, et al, IEEE Transactions on Evolutionary Computation., Vol. 6, p. 182-197 (2002)
High-speed permanent magnet synchronous motor (HSPMSM) is electromechanical energy conversion device with deep multi-physics field coupling, featuring small volume and high power density. During high-speed rotation, predicting critical speed is essential to prevent rotor bending resonance. Permanent magnet material necessitates the carbon fiber sleeve due to tensile stress limits. Multi-objective collaborative optimization is required to achieve near-limit material application. Traditional serial design of finite element models isolates dynamic constraints between various physical fields and neglects the dynamic cross-influences of nonlinear changes [1]. Surrogate model enables efficient multidisciplinary parallel optimization by predicting data from sample sets, crucial for multi-objective optimization [2]. However, multiple optimization objectives and large nonlinear search space of HSPMSM reduce model accuracy. This paper proposes an improved hybrid surrogate model composed of sub-models with the highest fitting accuracy for each variable, resulting in high global surrogate accuracy and wide range. Multidisciplinary optimization design is for a 100 kW, 20000 r/min HSPMSM, as shown in Fig.1(a) and (b). The objective variables include maximizing sleeve tangential stress and magnet radial stress within constraints, and minimizing core loss, copper loss, and torque ripple. Constraints include no-load back electromotive force and critical speed. The mathematical model is built. Sensitivity analysis using the LTSM-SOBOL model identifies six structural parameters for optimization. Optimal Latin Hypercube Sampling generates a highly space-filling sample set. Various surrogate models are trained using the sample set to establish a hybrid surrogate model, shown as Fig.1(c). NSGA-II is used for multidisciplinary optimization [3]. This approach effectively decreases the sleeve thickness and reduces losses, verified through finite element analysis, yielding a high-reliability and high-efficiency motor optimization solution, as shown in Fig.2(a) and (b). This method is applicable to multidisciplinary optimization of various types of PMSM.References: [1] G. Bramerdorfer, J. A. Tapia, J. J. Pyrhonen, et al, IEEE Transactions on Industrial Electronics., Vol. 65, p. 7672–7684 (2018) [2] B. Xie, Y. Zhang, Z. Xu, et al, IEEE Transactions on Energy Conversion., Vol. 38, p. 859–868 (2023) [3] K. Deb, A. Pratap, S. Agarwal, et al, IEEE Transactions on Evolutionary Computation., Vol. 6, p. 182-197 (2002)