An online state of health estimation method based on battery management system monitoring data
Summary A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent...
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| Veröffentlicht in: | International journal of energy research 2020-06, Vol.44 (8), p.6338-6349 |
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| container_end_page | 6349 |
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| container_title | International journal of energy research |
| container_volume | 44 |
| creator | Liu, Fang Liu, Xinyi Su, Weixing Lin, Hui Chen, Hanning He, Maowei |
| description | Summary
A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods. |
| doi_str_mv | 10.1002/er.5351 |
| format | Article |
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A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1002/er.5351</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Batteries ; battery mathematical model ; Computer applications ; Data base management systems ; Electric vehicles ; Engineering ; Genetic algorithms ; Independent variables ; Internet ; Mathematical models ; Monitoring ; NLS‐GA ; Parameter identification ; Power management ; SOH</subject><ispartof>International journal of energy research, 2020-06, Vol.44 (8), p.6338-6349</ispartof><rights>2020 John Wiley & Sons Ltd</rights><rights>2020 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3221-a291f2ae5ee772f038bdea4ca8796ba0df3748375964bf8a2073aae6cec85e53</citedby><cites>FETCH-LOGICAL-c3221-a291f2ae5ee772f038bdea4ca8796ba0df3748375964bf8a2073aae6cec85e53</cites><orcidid>0000-0002-5967-3424 ; 0000-0002-0482-3288</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fer.5351$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fer.5351$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,777,781,1413,27906,27907,45556,45557</link.rule.ids></links><search><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Liu, Xinyi</creatorcontrib><creatorcontrib>Su, Weixing</creatorcontrib><creatorcontrib>Lin, Hui</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>He, Maowei</creatorcontrib><title>An online state of health estimation method based on battery management system monitoring data</title><title>International journal of energy research</title><description>Summary
A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.</description><subject>Algorithms</subject><subject>Batteries</subject><subject>battery mathematical model</subject><subject>Computer applications</subject><subject>Data base management systems</subject><subject>Electric vehicles</subject><subject>Engineering</subject><subject>Genetic algorithms</subject><subject>Independent variables</subject><subject>Internet</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>NLS‐GA</subject><subject>Parameter identification</subject><subject>Power management</subject><subject>SOH</subject><issn>0363-907X</issn><issn>1099-114X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp10EtLw0AQAOBFFKxV_AsLHjxIdB9JNjmWUh9QEKSHnlwmyaRNSXbr7hbJv3drvXqagfmYFyG3nD1yxsQTusdMZvyMTDgry4TzdH1OJkzmMimZWl-SK-93jMUaVxPyOTPUmr4zSH2AgNS2dIvQhy1FH7oBQmcNHTBsbUMr8NhEHpMQ0I10AAMbHNAE6kcfcKCDNV2wrjMb2kCAa3LRQu_x5i9Oyep5sZq_Jsv3l7f5bJnUUgiegCh5KwAzRKVEy2RRNQhpDYUq8wpY00qVFlJlZZ5WbQGCKQmAeY11kWEmp-Tu1Hbv7NchLq539uBMnKhFyqVMcyFZVPcnVTvrvcNW71280I2aM338nUanj7-L8uEkv7sex_-YXnz86h96vHAZ</recordid><startdate>20200625</startdate><enddate>20200625</enddate><creator>Liu, Fang</creator><creator>Liu, Xinyi</creator><creator>Su, Weixing</creator><creator>Lin, Hui</creator><creator>Chen, Hanning</creator><creator>He, Maowei</creator><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>7TN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>F28</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-5967-3424</orcidid><orcidid>https://orcid.org/0000-0002-0482-3288</orcidid></search><sort><creationdate>20200625</creationdate><title>An online state of health estimation method based on battery management system monitoring data</title><author>Liu, Fang ; Liu, Xinyi ; Su, Weixing ; Lin, Hui ; Chen, Hanning ; He, Maowei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3221-a291f2ae5ee772f038bdea4ca8796ba0df3748375964bf8a2073aae6cec85e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Batteries</topic><topic>battery mathematical model</topic><topic>Computer applications</topic><topic>Data base management systems</topic><topic>Electric vehicles</topic><topic>Engineering</topic><topic>Genetic algorithms</topic><topic>Independent variables</topic><topic>Internet</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>NLS‐GA</topic><topic>Parameter identification</topic><topic>Power management</topic><topic>SOH</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Liu, Xinyi</creatorcontrib><creatorcontrib>Su, Weixing</creatorcontrib><creatorcontrib>Lin, Hui</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>He, Maowei</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>International journal of energy research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Fang</au><au>Liu, Xinyi</au><au>Su, Weixing</au><au>Lin, Hui</au><au>Chen, Hanning</au><au>He, Maowei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An online state of health estimation method based on battery management system monitoring data</atitle><jtitle>International journal of energy research</jtitle><date>2020-06-25</date><risdate>2020</risdate><volume>44</volume><issue>8</issue><spage>6338</spage><epage>6349</epage><pages>6338-6349</pages><issn>0363-907X</issn><eissn>1099-114X</eissn><abstract>Summary
A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/er.5351</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-5967-3424</orcidid><orcidid>https://orcid.org/0000-0002-0482-3288</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal of energy research, 2020-06, Vol.44 (8), p.6338-6349 |
| issn | 0363-907X 1099-114X |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Batteries battery mathematical model Computer applications Data base management systems Electric vehicles Engineering Genetic algorithms Independent variables Internet Mathematical models Monitoring NLS‐GA Parameter identification Power management SOH |
| title | An online state of health estimation method based on battery management system monitoring data |
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