中国电机工程学报2026，Vol.46Issue(5)：2057-2069,中插26,14.DOI:10.13334/j.0258-8013.pcsee.242203
基于几何相似性的迁移学习代理模型电机优化设计方法
Geometrical Similarity Transfer Learning-based Surrogate-assisted Model Optimization Design Method for Electric Machines
摘要
Abstract
By using precise analytical model data to assist numerical simulation data,constructing the high-quality training dataset for the surrogate model offers lower computational cost and enhances the efficiency of multi-objective optimization.However,existing precise analytical modeling methods often impose specific requirements on machine structures,limiting their applicability.To address this issue,the work proposes an optimization design method for motor surrogate models based on geometric similarity transfer learning.This method first utilizes the geometric similarity between machines' physical structures to construct similar motors that are able to establish precise analytical modeling.Subsequently,a hierarchical optimization strategy is adopted by partitioning the design-variable space into high-and low-sensitivity subspaces to enhance the consistency between the transfer-learned optimization results and those obtained from the original prototype.The high sensitivity parameter space with fewer variables is optimized using a conventional surrogate model established from finite element analysis(FEA)data,while the low sensitivity parameter space with more variables is optimized based on transfer learning(TL)to train a multi-fidelity surrogate model.The proposed method breaks the topological constraints of accurate analytical models,reduces the difficulty of analytical modeling for structurally complex machines,and significantly improves optimization efficiency for high-dimensional problems through hierarchical and multi-fidelity transfer learning,achieving substantial computational savings without sacrificing accuracy.The design method was applied to the multi-objective optimization design of a built-in consequent pole permanent magnet vernier motor,and prototype experimental results validated its effectiveness.关键词
几何相似性/迁移学习/代理模型/多目标优化设计/内置式交替极永磁游标电机Key words
geometrical similarity/transfer learning/surrogate model/multi-objective optimization design/built-in consequent pole permanent magnet vernier motor分类
信息技术与安全科学引用本文复制引用
李泽泉,马一鸣,刘黎阳,康皓宇,孙鲁,王晋,周理兵..基于几何相似性的迁移学习代理模型电机优化设计方法[J].中国电机工程学报,2026,46(5):2057-2069,中插26,14.基金项目
国家自然科学基金项目(重点项目)(52237002).Project Supported by National Natural Science Foundation of China(Key Program)(52237002). （重点项目）
