VP4-08 Automatic Design Method for Motor Topology Based on Data-Driven
Creators: Qiangren Xu
Shuhua Fang
Xiangru Lin
and Yicheng Wang
Publisher: name: IEEE
Year: 2025

In response to the critical need for diminished carbon emissions [1], the aviation industry has accelerated the development of multi-electric aircraft (MEA) technologies [2], [3]. The performance of these aircraft depends on the design of electric motors which is predicated on the accuracy of electromagnetic simulations in finite element method (FEM) [4], [5]. Despite the sophisticated analyses provided by simulation software like ANSYS and JMAG, there is an ongoing quest for streamlined design methodologies [6], [7]. This article introduces a Python-based JMAG enhancement, automating simulation workflows and laying the groundwork for AI-augmented motor design optimization, thereby enhancing efficiency and pioneering innovative avenues in motor design. Capitalizing on Python's scripting prowess, this article enhances JMAG via COM interface integration, automating motor design. The flowchart and specific execution process is shown in Fig. 1. By inputting detailed initial structural parameters, the encapsulation of the automated topology design method can be achieved, exemplified by the prevalent surface-mounted PMSM in MEA. A refined input schema ensures methodical topological accuracy. The integrative program is weighted by application criteria, quantitatively assesses motor performance, , computes an evaluative composite score, and streamlines design optimization. After the motor topology design is completed, electromagnetic parameters are automatically assigned to various parts of the topology, and the secondary development program automatically performs meshing and finite element analysis on the designed topology. The final motor topology and electromagnetic performance designed through data-driven automated design methods are shown in Fig. 2. The detailed description of the data-driven automated motor topology design method, encompassing its methodological exposition, evaluative metrics, and experimental verification, will be addressed in the full paper.References: [1] X. Li, D. Shi, Y. Li and X. Zhen, IEEE Trans. Syst. Man Cybern.: Syst., vol. 49, no. 6, pp. 1218-1227. [2] J. Ebersberger, M. Hagedorn, M. Lorenz et al., IEEE J. Emerging Sel. Top. Power Electron., vol. 10, no. 5, pp. 5264-5279. [3] B. Sarlioglu and C. T. Morris, IEEE Trans. Transport. Electrific., vol. 1, no. 1, pp. 54-64. [4] T. Kosaka, Y. Miyama, H. Ukaji et al., IPEC-Himeji 2022- ECCE Asia, Himeji, Japan, 2022, pp. 1990-1996. [5] Y. Hidaka, T. Sato and H. Igarashi, IEEE Trans. Magn., vol. 50, no. 2, pp. 617-620. [6] L. Yu et al., Proceedings - 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 4484-4488. [7] Y. Wu, T. Huang and H. Yu, IMCEC, Xi'an, China, 2018, pp. 197-201.
In response to the critical need for diminished carbon emissions [1], the aviation industry has accelerated the development of multi-electric aircraft (MEA) technologies [2], [3]. The performance of these aircraft depends on the design of electric motors which is predicated on the accuracy of electromagnetic simulations in finite element method (FEM) [4], [5]. Despite the sophisticated analyses provided by simulation software like ANSYS and JMAG, there is an ongoing quest for streamlined design methodologies [6], [7]. This article introduces a Python-based JMAG enhancement, automating simulation workflows and laying the groundwork for AI-augmented motor design optimization, thereby enhancing efficiency and pioneering innovative avenues in motor design. Capitalizing on Python's scripting prowess, this article enhances JMAG via COM interface integration, automating motor design. The flowchart and specific execution process is shown in Fig. 1. By inputting detailed initial structural parameters, the encapsulation of the automated topology design method can be achieved, exemplified by the prevalent surface-mounted PMSM in MEA. A refined input schema ensures methodical topological accuracy. The integrative program is weighted by application criteria, quantitatively assesses motor performance, , computes an evaluative composite score, and streamlines design optimization. After the motor topology design is completed, electromagnetic parameters are automatically assigned to various parts of the topology, and the secondary development program automatically performs meshing and finite element analysis on the designed topology. The final motor topology and electromagnetic performance designed through data-driven automated design methods are shown in Fig. 2. The detailed description of the data-driven automated motor topology design method, encompassing its methodological exposition, evaluative metrics, and experimental verification, will be addressed in the full paper.References: [1] X. Li, D. Shi, Y. Li and X. Zhen, IEEE Trans. Syst. Man Cybern.: Syst., vol. 49, no. 6, pp. 1218-1227. [2] J. Ebersberger, M. Hagedorn, M. Lorenz et al., IEEE J. Emerging Sel. Top. Power Electron., vol. 10, no. 5, pp. 5264-5279. [3] B. Sarlioglu and C. T. Morris, IEEE Trans. Transport. Electrific., vol. 1, no. 1, pp. 54-64. [4] T. Kosaka, Y. Miyama, H. Ukaji et al., IPEC-Himeji 2022- ECCE Asia, Himeji, Japan, 2022, pp. 1990-1996. [5] Y. Hidaka, T. Sato and H. Igarashi, IEEE Trans. Magn., vol. 50, no. 2, pp. 617-620. [6] L. Yu et al., Proceedings - 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 4484-4488. [7] Y. Wu, T. Huang and H. Yu, IMCEC, Xi'an, China, 2018, pp. 197-201.