VP4-02 A Multi-Topology and Multi-Objective Optimization System for PMSM Using AutoML-Based Surrogate Models
Creators: Jun Luo
Xiaoyong Zhu
Jiqi Wu
Zixuan Xiang
Wenhua Chen
Zhan Sun
Publisher: name: IEEE
Year: 2025

In recent years, Permanent Magnet Synchronous Motors (PMSMs) have been widely utilized across various fields due to their high torque density and high efficiency[1]. However, the diversity of design schemes necessitates extensive Finite Element Analysis (FEA) during the motor design phase to evaluate performance[2], [3]. To address this issue, this study proposes a multi-topology design system based on Automated Machine Learning (AutoML)[4]. This system can accurately predict the performance of motors with different topologies and adaptively optimize designs to meet specific requirements. Compared to traditional FEA methods, this system significantly enhances design efficiency, reducing the overall simulation time to less than 1/8000 of the time required by conventional FEA, and it enables parallel optimization of multi-topology motor designs. The system provides a unified representation of various rotor topologies and uses FEA to generate structured data for calculating torque and loss, as shown in Fig. 1. Initially, the structural parameters of different motor rotors are used as data inputs, and the motor characteristics obtained from FEA are used as data outputs. These multi-topology motor data are then combined into a dataset to train the automated machine learning model, resulting in a multi-dimensional motor model matrix. The predictive accuracy of the proposed automated machine learning model compared to other common machine learning models is shown in Fig. 2(1). Subsequently, this model replaces FEA for multi-objective optimization, ultimately producing optimized motor design schemes. A comparison of the pre- and post-optimization designs for the double V+1 topology is shown in Fig. 2(2). Overall, the system uses a unified dataset to describe multi-topology motors, employs an automated machine learning model to accurately predict their performance, and implements a rotor optimization design system that can achieve parallel optimization of multi-topology motors within 19 minutes.References: [1] J. Gu, W. Hua and W. Yu, "Surrogate Model-Based Multiobjective Optimization of High-Speed PM Synchronous Machine: Construction and Comparison," IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 678-688 (2023). [2] Z. Qian, T. Huang and Q. Wang, "Torque Ripple Reduction of PMSM Based on Modified DBN-DNN Surrogate Model," IEEE Transactions on Transportation Electrification, vol. 9, no. 2, pp. 2820-2829 (2023). [3] A. Khan, C. Midha and D. Lowther, "Reinforcement Learning for Topology Optimization of a Synchronous Reluctance Motor," IEEE Transactions on Magnetics, vol. 58, no. 9, pp. 1-4 (2022). [4] E. LeDell and S. Poirier, "H2O AutoML: Scalable Automatic Machine Learning," 7th ICML Workshop on Automated Machine Learning (AutoML) (2020). [Online]. Available:https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf.
In recent years, Permanent Magnet Synchronous Motors (PMSMs) have been widely utilized across various fields due to their high torque density and high efficiency[1]. However, the diversity of design schemes necessitates extensive Finite Element Analysis (FEA) during the motor design phase to evaluate performance[2], [3]. To address this issue, this study proposes a multi-topology design system based on Automated Machine Learning (AutoML)[4]. This system can accurately predict the performance of motors with different topologies and adaptively optimize designs to meet specific requirements. Compared to traditional FEA methods, this system significantly enhances design efficiency, reducing the overall simulation time to less than 1/8000 of the time required by conventional FEA, and it enables parallel optimization of multi-topology motor designs. The system provides a unified representation of various rotor topologies and uses FEA to generate structured data for calculating torque and loss, as shown in Fig. 1. Initially, the structural parameters of different motor rotors are used as data inputs, and the motor characteristics obtained from FEA are used as data outputs. These multi-topology motor data are then combined into a dataset to train the automated machine learning model, resulting in a multi-dimensional motor model matrix. The predictive accuracy of the proposed automated machine learning model compared to other common machine learning models is shown in Fig. 2(1). Subsequently, this model replaces FEA for multi-objective optimization, ultimately producing optimized motor design schemes. A comparison of the pre- and post-optimization designs for the double V+1 topology is shown in Fig. 2(2). Overall, the system uses a unified dataset to describe multi-topology motors, employs an automated machine learning model to accurately predict their performance, and implements a rotor optimization design system that can achieve parallel optimization of multi-topology motors within 19 minutes.References: [1] J. Gu, W. Hua and W. Yu, "Surrogate Model-Based Multiobjective Optimization of High-Speed PM Synchronous Machine: Construction and Comparison," IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 678-688 (2023). [2] Z. Qian, T. Huang and Q. Wang, "Torque Ripple Reduction of PMSM Based on Modified DBN-DNN Surrogate Model," IEEE Transactions on Transportation Electrification, vol. 9, no. 2, pp. 2820-2829 (2023). [3] A. Khan, C. Midha and D. Lowther, "Reinforcement Learning for Topology Optimization of a Synchronous Reluctance Motor," IEEE Transactions on Magnetics, vol. 58, no. 9, pp. 1-4 (2022). [4] E. LeDell and S. Poirier, "H2O AutoML: Scalable Automatic Machine Learning," 7th ICML Workshop on Automated Machine Learning (AutoML) (2020). [Online]. Available:https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf.