VP3-01 A Novel Optimization Method of Topology for Improving Torque Quality in Permanent Magnet Synchronous Machine
Creators: Xiaoyu Liang
Mingqiao Wang*
Yong Liu*
Ping Zheng and Wanquan Li
Publisher: name: IEEE
Year: 2025

High performance machines are the guarantee for the development of industry and economy. The demand for machine performance and design efficiency drives the continuous improvement of machine design and optimization. Many scholars have proposed or improved methods to further improve these processes. Structural optimization is mainly divided into parameter optimization (PO) and topology optimization (TO) [1]. TOs allow the boundaries of each material to be randomly generated. TOs are being attempted to be applied in the topology design to explore performance boundaries [2]. Many machines with better performance that may have never been considered in traditional methods are discovered by TOs. However, the high-degree freedom topology generation based on TOs also brings disorder and inefficiency, and the final result also needs to be obtained after extensive manual screening. Existing research mainly focuses on the algorithm, with little study on how to combine expert experience in conventional method with topology generation without strong constraints in TOs. By this, the design efficiency and performance of machine can be further enhanced. The previous result of TOs can also be better transferred to the research of new structures. The proposed method combining the above professional knowledge and TO is shown in Fig. 1. Compared with the existing method, the improved algorithm assumes several optimization paths based on professional knowledge firstly, multi-objective penalty functions are used to improve the generation direction after each round of result, and the adjustment region sequence is determined quickly by the analytic model. Taking the Permanent Magnet Synchronous Machine as an example, the schematic diagram of structure optimization is shown in Fig. 2. The minimum unit of random generation is determined based on the grid. Guided by the expert experience, the data-driven iterative algorithm optimizes the topology automatically, and extracts the optimal substructure to provide prior data for future design.References: [1] H. Sato and H. Igarashi, "Automatic Design of PM Motor Using Monte Carlo Tree Search in Conjunction With Topology Optimization," IEEE Transactions on Magnetics, vol. 58, no. 9: 7200504 (2022) [2] N. Takahashi, S. Nakazaki and D. Miyagi, "Optimization of Electromagnetic and Magnetic Shielding Using ON/OFF Method," IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 3153-3156 (2010)
High performance machines are the guarantee for the development of industry and economy. The demand for machine performance and design efficiency drives the continuous improvement of machine design and optimization. Many scholars have proposed or improved methods to further improve these processes. Structural optimization is mainly divided into parameter optimization (PO) and topology optimization (TO) [1]. TOs allow the boundaries of each material to be randomly generated. TOs are being attempted to be applied in the topology design to explore performance boundaries [2]. Many machines with better performance that may have never been considered in traditional methods are discovered by TOs. However, the high-degree freedom topology generation based on TOs also brings disorder and inefficiency, and the final result also needs to be obtained after extensive manual screening. Existing research mainly focuses on the algorithm, with little study on how to combine expert experience in conventional method with topology generation without strong constraints in TOs. By this, the design efficiency and performance of machine can be further enhanced. The previous result of TOs can also be better transferred to the research of new structures. The proposed method combining the above professional knowledge and TO is shown in Fig. 1. Compared with the existing method, the improved algorithm assumes several optimization paths based on professional knowledge firstly, multi-objective penalty functions are used to improve the generation direction after each round of result, and the adjustment region sequence is determined quickly by the analytic model. Taking the Permanent Magnet Synchronous Machine as an example, the schematic diagram of structure optimization is shown in Fig. 2. The minimum unit of random generation is determined based on the grid. Guided by the expert experience, the data-driven iterative algorithm optimizes the topology automatically, and extracts the optimal substructure to provide prior data for future design.References: [1] H. Sato and H. Igarashi, "Automatic Design of PM Motor Using Monte Carlo Tree Search in Conjunction With Topology Optimization," IEEE Transactions on Magnetics, vol. 58, no. 9: 7200504 (2022) [2] N. Takahashi, S. Nakazaki and D. Miyagi, "Optimization of Electromagnetic and Magnetic Shielding Using ON/OFF Method," IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 3153-3156 (2010)