Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm
Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. Firstly, the power density, electromagnetic torque, cogging torque, and...
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| Veröffentlicht in: | Electronics (Basel) 2023-10, Vol.12 (19), p.4004 |
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| creator | Li, Guoshuai Sun, Huiqin Hu, Weiguang Li, Ying Bai, Yongqiang Guo, Yingjun |
| description | Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. Firstly, the power density, electromagnetic torque, cogging torque, and torque fluctuation coefficient were used as optimization targets based on parametric analysis data of 14 motor structure variables, where parametric sensitivity analysis helped select eight optimization variables. Secondly, the motor prediction model was fitted using the genetic algorithm–back propagation (GA-BP) neural network. Finally, non-dominated sorting genetic algorithm-III (NSGA-III), based on the reference points, was used to find the optimization of the prediction model and complete the multi-objective optimization design of the external rotor PMA-SynRM with eight inputs and four outputs. A comparative analysis of the electromagnetic performance of the motor before and after optimization verifies the feasibility of optimizing the motor using the composite algorithm. This paper provides an analytical tool for the multi-parameter and multi-objective PMA-SynRM optimization design. |
| doi_str_mv | 10.3390/electronics12194004 |
| format | Article |
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Firstly, the power density, electromagnetic torque, cogging torque, and torque fluctuation coefficient were used as optimization targets based on parametric analysis data of 14 motor structure variables, where parametric sensitivity analysis helped select eight optimization variables. Secondly, the motor prediction model was fitted using the genetic algorithm–back propagation (GA-BP) neural network. Finally, non-dominated sorting genetic algorithm-III (NSGA-III), based on the reference points, was used to find the optimization of the prediction model and complete the multi-objective optimization design of the external rotor PMA-SynRM with eight inputs and four outputs. A comparative analysis of the electromagnetic performance of the motor before and after optimization verifies the feasibility of optimizing the motor using the composite algorithm. This paper provides an analytical tool for the multi-parameter and multi-objective PMA-SynRM optimization design.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12194004</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Back propagation networks ; Design optimization ; Design techniques ; Electric motors ; Genetic algorithms ; Magnetic fields ; Multiple objective analysis ; Neural networks ; Objectives ; Parameter sensitivity ; Parametric analysis ; Permanent magnets ; Prediction models ; Reluctance ; Rotors ; Sensitivity analysis ; Sorting algorithms ; Torque ; Variables</subject><ispartof>Electronics (Basel), 2023-10, Vol.12 (19), p.4004</ispartof><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c322t-5e234006d61a041955a0ff78b174cffc3b93bbcb74cdde1e97cd64742c9b218c3</citedby><cites>FETCH-LOGICAL-c322t-5e234006d61a041955a0ff78b174cffc3b93bbcb74cdde1e97cd64742c9b218c3</cites><orcidid>0009-0008-4224-6830 ; 0000-0002-6453-6387 ; 0009-0009-9978-6907</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Li, Guoshuai</creatorcontrib><creatorcontrib>Sun, Huiqin</creatorcontrib><creatorcontrib>Hu, Weiguang</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Bai, Yongqiang</creatorcontrib><creatorcontrib>Guo, Yingjun</creatorcontrib><title>Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm</title><title>Electronics (Basel)</title><description>Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. Firstly, the power density, electromagnetic torque, cogging torque, and torque fluctuation coefficient were used as optimization targets based on parametric analysis data of 14 motor structure variables, where parametric sensitivity analysis helped select eight optimization variables. Secondly, the motor prediction model was fitted using the genetic algorithm–back propagation (GA-BP) neural network. Finally, non-dominated sorting genetic algorithm-III (NSGA-III), based on the reference points, was used to find the optimization of the prediction model and complete the multi-objective optimization design of the external rotor PMA-SynRM with eight inputs and four outputs. A comparative analysis of the electromagnetic performance of the motor before and after optimization verifies the feasibility of optimizing the motor using the composite algorithm. This paper provides an analytical tool for the multi-parameter and multi-objective PMA-SynRM optimization design.</description><subject>Back propagation networks</subject><subject>Design optimization</subject><subject>Design techniques</subject><subject>Electric motors</subject><subject>Genetic algorithms</subject><subject>Magnetic fields</subject><subject>Multiple objective analysis</subject><subject>Neural networks</subject><subject>Objectives</subject><subject>Parameter sensitivity</subject><subject>Parametric analysis</subject><subject>Permanent magnets</subject><subject>Prediction models</subject><subject>Reluctance</subject><subject>Rotors</subject><subject>Sensitivity analysis</subject><subject>Sorting algorithms</subject><subject>Torque</subject><subject>Variables</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkMtOwzAQRSMEElXpF7CxxDrgV-N4WUp5SK2KCqwjx5m0rhK72A6i_AV_TAosWDCbmZHuHM29SXJO8CVjEl9BAzp6Z40OhBLJMeZHyYBiIVNJJT3-M58moxC2uC9JWM7wIPlcdE006bLc9hDzBmi5i6Y1HyoaZ9ENBLO2yNUobgDN3iN4qxq0ctF59Ai-VRZsRAu1thDTSQgmRKjQ097qTf-R6wJaQdPpqKwGtPg-u1ahl_TwA3Lq2p0LJgKaNGvnTdy0Z8lJrZoAo98-TF5uZ8_T-3S-vHuYTuapZpTGdAyU9VazKiMKcyLHY4XrWuQlEVzXtWalZGWpy36rKiAgha4yLjjVsqQk12yYXPxwd969dhBisXXdwV4oaC4yTgXHolexH5X2LgQPdbHzplV-XxBcHOIv_omffQHoWn7K</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Li, Guoshuai</creator><creator>Sun, Huiqin</creator><creator>Hu, Weiguang</creator><creator>Li, Ying</creator><creator>Bai, Yongqiang</creator><creator>Guo, Yingjun</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0009-0008-4224-6830</orcidid><orcidid>https://orcid.org/0000-0002-6453-6387</orcidid><orcidid>https://orcid.org/0009-0009-9978-6907</orcidid></search><sort><creationdate>20231001</creationdate><title>Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm</title><author>Li, Guoshuai ; Sun, Huiqin ; Hu, Weiguang ; Li, Ying ; Bai, Yongqiang ; Guo, Yingjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c322t-5e234006d61a041955a0ff78b174cffc3b93bbcb74cdde1e97cd64742c9b218c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Back propagation networks</topic><topic>Design optimization</topic><topic>Design techniques</topic><topic>Electric motors</topic><topic>Genetic algorithms</topic><topic>Magnetic fields</topic><topic>Multiple objective analysis</topic><topic>Neural networks</topic><topic>Objectives</topic><topic>Parameter sensitivity</topic><topic>Parametric analysis</topic><topic>Permanent magnets</topic><topic>Prediction models</topic><topic>Reluctance</topic><topic>Rotors</topic><topic>Sensitivity analysis</topic><topic>Sorting algorithms</topic><topic>Torque</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Guoshuai</creatorcontrib><creatorcontrib>Sun, Huiqin</creatorcontrib><creatorcontrib>Hu, Weiguang</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Bai, Yongqiang</creatorcontrib><creatorcontrib>Guo, Yingjun</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Guoshuai</au><au>Sun, Huiqin</au><au>Hu, Weiguang</au><au>Li, Ying</au><au>Bai, Yongqiang</au><au>Guo, Yingjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-10-01</date><risdate>2023</risdate><volume>12</volume><issue>19</issue><spage>4004</spage><pages>4004-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. 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| subjects | Back propagation networks Design optimization Design techniques Electric motors Genetic algorithms Magnetic fields Multiple objective analysis Neural networks Objectives Parameter sensitivity Parametric analysis Permanent magnets Prediction models Reluctance Rotors Sensitivity analysis Sorting algorithms Torque Variables |
| title | Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm |
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