Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning
Dear editor, This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning. Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH...
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| Veröffentlicht in: | IEEE/CAA journal of automatica sinica 2022-08, Vol.9 (8), p.1540-1542 |
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| container_title | IEEE/CAA journal of automatica sinica |
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| creator | Wang, Yujie Li, Kaiquan Chen, Zonghai |
| description | Dear editor, This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning. Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH) prediction. The features of charging data including the constant current time, constant voltage time, and the total charging time are selected as the input characteristics of the network to estimate SOH. Moreover, the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity. Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods. |
| doi_str_mv | 10.1109/JAS.2022.105779 |
| format | Article |
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Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH) prediction. The features of charging data including the constant current time, constant voltage time, and the total charging time are selected as the input characteristics of the network to estimate SOH. Moreover, the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity. Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods.</description><identifier>ISSN: 2329-9266</identifier><identifier>EISSN: 2329-9274</identifier><identifier>DOI: 10.1109/JAS.2022.105779</identifier><identifier>CODEN: IJASJC</identifier><language>eng</language><publisher>Piscataway: Chinese Association of Automation (CAA)</publisher><subject>Battery cycles ; Charging ; Learning ; Prognosis</subject><ispartof>IEEE/CAA journal of automatica sinica, 2022-08, Vol.9 (8), p.1540-1542</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-93111532db4d86f82af06b1e96bb00b9cb8844a7be28cfd44506e6f40bffdae13</citedby><cites>FETCH-LOGICAL-c295t-93111532db4d86f82af06b1e96bb00b9cb8844a7be28cfd44506e6f40bffdae13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/zdhxb-ywb/zdhxb-ywb.jpg</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9849150$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9849150$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Yujie</creatorcontrib><creatorcontrib>Li, Kaiquan</creatorcontrib><creatorcontrib>Chen, Zonghai</creatorcontrib><title>Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning</title><title>IEEE/CAA journal of automatica sinica</title><addtitle>JAS</addtitle><description>Dear editor, This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning. Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH) prediction. The features of charging data including the constant current time, constant voltage time, and the total charging time are selected as the input characteristics of the network to estimate SOH. Moreover, the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity. Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods.</description><subject>Battery cycles</subject><subject>Charging</subject><subject>Learning</subject><subject>Prognosis</subject><issn>2329-9266</issn><issn>2329-9274</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpFkEFPwjAYhhejiQQ5e_DSxJsJ0HZdtx6BiGgwmqDnpl2_wsjosB0i_nqHM3j63sPzvl_yRNE1wQNCsBg-jRYDiikdEJykqTiLOjSmoi9oys5PmfPLqBfCGmNMaJJywTqRGqu6Bn9A011ZonlhAU0OeQnoWTm1hA24Giln0AxUWa_Qq6-WrgpFQGMVwKDKoUlZ7QxagP8scvhlx75SBs1BeVe45VV0YVUZoPd3u9H79P5tMuvPXx4eJ6N5P6ciqfsiJoQkMTWamYzbjCqLuSYguNYYa5HrLGNMpRpollvDWII5cMuwttYoIHE3umt398pZ5ZZyXe28az7Kb7P60vKw10dDOMNENPBtC2999bGDUP_TlIs04XFMeUMNWyr3VQgerNz6YqP8QRIsj95l410eV2XrvWnctI0CAE60yJggCY5_ABg-fTI</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Wang, Yujie</creator><creator>Li, Kaiquan</creator><creator>Chen, Zonghai</creator><general>Chinese Association of Automation (CAA)</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Department of Automation,University of Science and Technology of China,Hefei 230027,China</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20220801</creationdate><title>Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning</title><author>Wang, Yujie ; Li, Kaiquan ; Chen, Zonghai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-93111532db4d86f82af06b1e96bb00b9cb8844a7be28cfd44506e6f40bffdae13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Battery cycles</topic><topic>Charging</topic><topic>Learning</topic><topic>Prognosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yujie</creatorcontrib><creatorcontrib>Li, Kaiquan</creatorcontrib><creatorcontrib>Chen, Zonghai</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>IEEE/CAA journal of automatica sinica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Yujie</au><au>Li, Kaiquan</au><au>Chen, Zonghai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning</atitle><jtitle>IEEE/CAA journal of automatica sinica</jtitle><stitle>JAS</stitle><date>2022-08-01</date><risdate>2022</risdate><volume>9</volume><issue>8</issue><spage>1540</spage><epage>1542</epage><pages>1540-1542</pages><issn>2329-9266</issn><eissn>2329-9274</eissn><coden>IJASJC</coden><abstract>Dear editor, This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning. Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH) prediction. The features of charging data including the constant current time, constant voltage time, and the total charging time are selected as the input characteristics of the network to estimate SOH. Moreover, the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity. Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods.</abstract><cop>Piscataway</cop><pub>Chinese Association of Automation (CAA)</pub><doi>10.1109/JAS.2022.105779</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Battery cycles Charging Learning Prognosis |
| title | Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning |
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