Article(id=1309640091505623289, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250501, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1761321600000, revisedDateStr=2025-10-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172129750, onlineDateStr=2026-09-23, pubDate=1776960000000, pubDateStr=2026-04-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172129750, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172129750, creator=13701087609, updateTime=1790172129750, updator=13701087609, issue=Issue{id=1309640086191432626, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1776960000000', pubDateStr='2026-04-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172128484, creator='13701087609', updateTime=1790232864374, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894831258825016, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894831258825017, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=42, endPage=53, ext={EN=ArticleExt(id=1309640091673395450, articleId=1309640091505623289, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Structural Parameters Optimization of High-Speed Permanent Magnet Synchronous Motors Based on Deep Learning Models, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem of large torque ripple during the operation of high-speed motors, this paper starts from the perspective of motor structure optimization, takes key geometric parameters such as stator slot width, permanent magnet thickness, sheath thickness and pole arc coefficient as optimization variables, and aims to minimize torque ripple and maximize average torque as optimization goals, establishing a high-precision mapping model between structural parameters and motor performance. Firstly, the Hodrick-Prescott decomposition method is adopted to separate the trend and periodic components of the torque data in order to extract the key features; Secondly, a BiLSTM deep learning model integrating the cross-attention mechanism is constructed to characterize the nonlinear characteristics of the motor. On this basis, the improved butterfly optimization algorithm is introduced to optimize the model hyperparameters and obtain the optimal combination of structural parameters. Finally, the effectiveness of the proposed method is verified through simulation and experiments. The results show that this method can significantly reduce the torque pulsation of the motor and improve the smoothness and reliability of high-speed motor operation.

, authors=Yongxin Gao, Hongsheng Li, Zeqi Li, authorsList=Yongxin Gao, Hongsheng Li, Zeqi Li, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640091908276475, articleId=1309640091505623289, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于深度学习模型的高速永磁同步电机结构参数优化, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对高速电机运行过程中转矩脉动较大的问题，从电机结构优化角度出发，以定子槽宽、永磁体厚度、护套厚度及极弧系数等关键几何参数为优化变量，以转矩脉动最小化和平均转矩最大化为优化目标，建立结构参数与电机性能之间的高精度映射模型。首先，采用Hodrick-Prescott分解方法对转矩数据进行趋势与周期成分分离，以提取关键特征；其次，构建融合交叉注意力机制的BiLSTM深度学习模型，以表征电机非线性特性；在此基础上，引入改进蝴蝶优化算法对模型超参数进行寻优，并提出HP-BiLSTM-CA-IBOA组合预测模型选取电机参数，从而获得最优结构参数组合。最后，通过仿真与试验验证所提方法的有效性。结果表明，该方法可显著降低电机转矩脉动，提高高速电机运行的平稳性与可靠性。

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