Dario Barri;Federico Soresini;Massimiliano Gobbi;Antonino di Gerlando;Gianpiero Mastinu
{"title":"牵引电动机的自适应Pareto算法优化设计","authors":"Dario Barri;Federico Soresini;Massimiliano Gobbi;Antonino di Gerlando;Gianpiero Mastinu","doi":"10.1109/TVT.2025.3532752","DOIUrl":null,"url":null,"abstract":"This paper deals with an adaptive multi-objective optimisation process for the design of electric motors by means of supervised learning techniques. The process is based on a multi-objective optimisation approach which involves many objective functions (mass, rotor inertia, average total losses), constraints (geometrical feasibility, current and voltage limit, torque-speed profile, thermal constraints) and a large number of design variables (rotor, stator and winding parameters) that describe the physical properties of the motor. The design of computer experiments is based on a Low Discrepancy Sequence (LDS).The electric motor performance indices are evaluated through multiphysics simulations (electromagnetic, thermal) carried out by Motor-CAD<bold>™</b> software. Artificial Intelligence (AI) is adopted to approximate the physical model behaviour. Artificial Neural Networks (ANN) are exploited, in this way a large set of design variables combinations can be investigated with a reasonable computational effort. Since the multi-objective optimisation of electric motor is particularly complex, a special algorithm had to be developed to find Pareto-Optimal solutions. A new <italic>Adaptive Pareto Algorithm</i> allows to reduce the computational cost and to achieve an even distribution of the optimal solutions in the Pareto optimal front. The algorithm is particularly effective when it is integrated with a global approximation model, the infill operation allows to improve the metamodel approximation. The derived electric motors show the best compromise performances among millions of possible configurations.","PeriodicalId":13421,"journal":{"name":"IEEE Transactions on Vehicular Technology","volume":"74 6","pages":"8890-8906"},"PeriodicalIF":7.1000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Design of Traction Electric Motors by a New Adaptive Pareto Algorithm\",\"authors\":\"Dario Barri;Federico Soresini;Massimiliano Gobbi;Antonino di Gerlando;Gianpiero Mastinu\",\"doi\":\"10.1109/TVT.2025.3532752\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with an adaptive multi-objective optimisation process for the design of electric motors by means of supervised learning techniques. The process is based on a multi-objective optimisation approach which involves many objective functions (mass, rotor inertia, average total losses), constraints (geometrical feasibility, current and voltage limit, torque-speed profile, thermal constraints) and a large number of design variables (rotor, stator and winding parameters) that describe the physical properties of the motor. The design of computer experiments is based on a Low Discrepancy Sequence (LDS).The electric motor performance indices are evaluated through multiphysics simulations (electromagnetic, thermal) carried out by Motor-CAD<bold>™</b> software. Artificial Intelligence (AI) is adopted to approximate the physical model behaviour. Artificial Neural Networks (ANN) are exploited, in this way a large set of design variables combinations can be investigated with a reasonable computational effort. Since the multi-objective optimisation of electric motor is particularly complex, a special algorithm had to be developed to find Pareto-Optimal solutions. A new <italic>Adaptive Pareto Algorithm</i> allows to reduce the computational cost and to achieve an even distribution of the optimal solutions in the Pareto optimal front. The algorithm is particularly effective when it is integrated with a global approximation model, the infill operation allows to improve the metamodel approximation. The derived electric motors show the best compromise performances among millions of possible configurations.\",\"PeriodicalId\":13421,\"journal\":{\"name\":\"IEEE Transactions on Vehicular Technology\",\"volume\":\"74 6\",\"pages\":\"8890-8906\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2025-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Vehicular Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10851401/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Vehicular Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10851401/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Optimal Design of Traction Electric Motors by a New Adaptive Pareto Algorithm
This paper deals with an adaptive multi-objective optimisation process for the design of electric motors by means of supervised learning techniques. The process is based on a multi-objective optimisation approach which involves many objective functions (mass, rotor inertia, average total losses), constraints (geometrical feasibility, current and voltage limit, torque-speed profile, thermal constraints) and a large number of design variables (rotor, stator and winding parameters) that describe the physical properties of the motor. The design of computer experiments is based on a Low Discrepancy Sequence (LDS).The electric motor performance indices are evaluated through multiphysics simulations (electromagnetic, thermal) carried out by Motor-CAD™ software. Artificial Intelligence (AI) is adopted to approximate the physical model behaviour. Artificial Neural Networks (ANN) are exploited, in this way a large set of design variables combinations can be investigated with a reasonable computational effort. Since the multi-objective optimisation of electric motor is particularly complex, a special algorithm had to be developed to find Pareto-Optimal solutions. A new Adaptive Pareto Algorithm allows to reduce the computational cost and to achieve an even distribution of the optimal solutions in the Pareto optimal front. The algorithm is particularly effective when it is integrated with a global approximation model, the infill operation allows to improve the metamodel approximation. The derived electric motors show the best compromise performances among millions of possible configurations.
期刊介绍:
The scope of the Transactions is threefold (which was approved by the IEEE Periodicals Committee in 1967) and is published on the journal website as follows: Communications: The use of mobile radio on land, sea, and air, including cellular radio, two-way radio, and one-way radio, with applications to dispatch and control vehicles, mobile radiotelephone, radio paging, and status monitoring and reporting. Related areas include spectrum usage, component radio equipment such as cavities and antennas, compute control for radio systems, digital modulation and transmission techniques, mobile radio circuit design, radio propagation for vehicular communications, effects of ignition noise and radio frequency interference, and consideration of the vehicle as part of the radio operating environment. Transportation Systems: The use of electronic technology for the control of ground transportation systems including, but not limited to, traffic aid systems; traffic control systems; automatic vehicle identification, location, and monitoring systems; automated transport systems, with single and multiple vehicle control; and moving walkways or people-movers. Vehicular Electronics: The use of electronic or electrical components and systems for control, propulsion, or auxiliary functions, including but not limited to, electronic controls for engineer, drive train, convenience, safety, and other vehicle systems; sensors, actuators, and microprocessors for onboard use; electronic fuel control systems; vehicle electrical components and systems collision avoidance systems; electromagnetic compatibility in the vehicle environment; and electric vehicles and controls.