{"title":"Energy Efficiency Optimization Design for Cycle Position Servo PMSM Based on Operating Energy Consumption Model","authors":"Bin Yuan;Hui Li;Xuewei Xiang;Hao Zhou","doi":"10.1109/OJIES.2025.3555606","DOIUrl":null,"url":null,"abstract":"Motor efficient design is an important measure to reduce the energy consumption of servo motor system. Existing methods typically focus on optimizing the efficiency at specific operating points or the proportion of the high-efficiency region, making it difficult to quantify the matching of the position servo motor's periodic wide-domain operating conditions under trajectory planning. In this article, an energy efficiency optimization design method for position servo permanent magnet synchronous motor (PMSM) based on a cycle operating energy consumption model is proposed. First, the periodic operating states of PMSM under position trajectory planning are characterized by the speed-torque operating curve. A neural network mapping between PMSM full-domain dynamic losses and speed-torque-temperature is constructed based on finite element data. Combined with the physical analytical model of mechanical power and friction, a data-model driven precise model is established, enabling quantitative evaluation of the cycle energy consumption with different PMSM design schemes; then, taking cycle operating energy consumption and peak torque as optimization objectives, the optimal Latin hypercube sampling method is employed to generate finite element optimization data samples. Dimension reduction of design variables is performed through correlation analysis, followed by the establishment of a precise response surface model for optimization objectives and significant variables. The optimal design scheme after global optimization is quickly solved by the evolutionary algorithm. Finally, the effectiveness of the proposed method is verified through simulation and prototype experiments.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"6 ","pages":"591-602"},"PeriodicalIF":5.2000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949210","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10949210/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Motor efficient design is an important measure to reduce the energy consumption of servo motor system. Existing methods typically focus on optimizing the efficiency at specific operating points or the proportion of the high-efficiency region, making it difficult to quantify the matching of the position servo motor's periodic wide-domain operating conditions under trajectory planning. In this article, an energy efficiency optimization design method for position servo permanent magnet synchronous motor (PMSM) based on a cycle operating energy consumption model is proposed. First, the periodic operating states of PMSM under position trajectory planning are characterized by the speed-torque operating curve. A neural network mapping between PMSM full-domain dynamic losses and speed-torque-temperature is constructed based on finite element data. Combined with the physical analytical model of mechanical power and friction, a data-model driven precise model is established, enabling quantitative evaluation of the cycle energy consumption with different PMSM design schemes; then, taking cycle operating energy consumption and peak torque as optimization objectives, the optimal Latin hypercube sampling method is employed to generate finite element optimization data samples. Dimension reduction of design variables is performed through correlation analysis, followed by the establishment of a precise response surface model for optimization objectives and significant variables. The optimal design scheme after global optimization is quickly solved by the evolutionary algorithm. Finally, the effectiveness of the proposed method is verified through simulation and prototype experiments.
期刊介绍:
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