Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications
This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been develop...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on energy conversion 2014-06, Vol.29 (2), p.332-343 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 343 |
|---|---|
| container_issue | 2 |
| container_start_page | 332 |
| container_title | IEEE transactions on energy conversion |
| container_volume | 29 |
| creator | Thirugnanam, Kannan Ezhil Reena, Joy T. P. Singh, Mukesh Kumar, Praveen |
| description | This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics. |
| doi_str_mv | 10.1109/TEC.2014.2298460 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TEC_2014_2298460</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6716979</ieee_id><sourcerecordid>3377660681</sourcerecordid><originalsourceid>FETCH-LOGICAL-c324t-51746fb7648fe2f9e187f91918f7d0dac3890c1d963ed0b85aa6965b15c9579b3</originalsourceid><addsrcrecordid>eNpdkE1rAjEURUNpodZ2X-gm0E03Y_NmJh9vacVaQelGuytDnEl0ZJzYZFz47xtRuujqweXcy-MQ8ghsAMDwdTEeDVIG-SBNUeWCXZEecK4Sxjhekx5TiicKBd6SuxC2LJI8hR75nutuY3a6q0vd0LmrTFO3a-osndXJ1LX0TXed8Ue6DKd8YloTUTps1s7X3WZHh_u9d7rcUOs8_Uonp6CJY13t2nBPbqxugnm43D5Zvo8Xo49k9jmZjoazpMzSvEs4yFzYlRS5sia1aEBJi4CgrKxYpctMISuhQpGZiq0U11qg4CvgJXKJq6xPXs678ZefgwldsatDaZpGt8YdQhFNoASAVEX0-R-6dQffxu8ilXNUIpciUuxMld6F4I0t9r7eaX8sgBUn30X0XZx8FxffsfJ0rtTGmD9cSBAoMfsFevJ6RQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1545986476</pqid></control><display><type>article</type><title>Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications</title><source>IEEE Electronic Library (IEL)</source><creator>Thirugnanam, Kannan ; Ezhil Reena, Joy T. P. ; Singh, Mukesh ; Kumar, Praveen</creator><creatorcontrib>Thirugnanam, Kannan ; Ezhil Reena, Joy T. P. ; Singh, Mukesh ; Kumar, Praveen</creatorcontrib><description>This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics.</description><identifier>ISSN: 0885-8969</identifier><identifier>EISSN: 1558-0059</identifier><identifier>DOI: 10.1109/TEC.2014.2298460</identifier><identifier>CODEN: ITCNE4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Batteries ; capacity loss ; Charge ; Circuits ; Control algorithms ; Discharges (electric) ; Electric charge ; Electric vehicles ; electric vehicles (EVs) ; genetic algorithm (GA) ; Genetic algorithms ; Integrated circuit modeling ; Lithium-ion batteries ; Mathematical model ; Mathematical models ; Polynomials ; vehicle-to-grid (V2G)</subject><ispartof>IEEE transactions on energy conversion, 2014-06, Vol.29 (2), p.332-343</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-51746fb7648fe2f9e187f91918f7d0dac3890c1d963ed0b85aa6965b15c9579b3</citedby><cites>FETCH-LOGICAL-c324t-51746fb7648fe2f9e187f91918f7d0dac3890c1d963ed0b85aa6965b15c9579b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6716979$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6716979$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Thirugnanam, Kannan</creatorcontrib><creatorcontrib>Ezhil Reena, Joy T. P.</creatorcontrib><creatorcontrib>Singh, Mukesh</creatorcontrib><creatorcontrib>Kumar, Praveen</creatorcontrib><title>Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications</title><title>IEEE transactions on energy conversion</title><addtitle>TEC</addtitle><description>This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics.</description><subject>Algorithms</subject><subject>Batteries</subject><subject>capacity loss</subject><subject>Charge</subject><subject>Circuits</subject><subject>Control algorithms</subject><subject>Discharges (electric)</subject><subject>Electric charge</subject><subject>Electric vehicles</subject><subject>electric vehicles (EVs)</subject><subject>genetic algorithm (GA)</subject><subject>Genetic algorithms</subject><subject>Integrated circuit modeling</subject><subject>Lithium-ion batteries</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Polynomials</subject><subject>vehicle-to-grid (V2G)</subject><issn>0885-8969</issn><issn>1558-0059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1rAjEURUNpodZ2X-gm0E03Y_NmJh9vacVaQelGuytDnEl0ZJzYZFz47xtRuujqweXcy-MQ8ghsAMDwdTEeDVIG-SBNUeWCXZEecK4Sxjhekx5TiicKBd6SuxC2LJI8hR75nutuY3a6q0vd0LmrTFO3a-osndXJ1LX0TXed8Ue6DKd8YloTUTps1s7X3WZHh_u9d7rcUOs8_Uonp6CJY13t2nBPbqxugnm43D5Zvo8Xo49k9jmZjoazpMzSvEs4yFzYlRS5sia1aEBJi4CgrKxYpctMISuhQpGZiq0U11qg4CvgJXKJq6xPXs678ZefgwldsatDaZpGt8YdQhFNoASAVEX0-R-6dQffxu8ilXNUIpciUuxMld6F4I0t9r7eaX8sgBUn30X0XZx8FxffsfJ0rtTGmD9cSBAoMfsFevJ6RQ</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Thirugnanam, Kannan</creator><creator>Ezhil Reena, Joy T. P.</creator><creator>Singh, Mukesh</creator><creator>Kumar, Praveen</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>7SC</scope><scope>F28</scope><scope>H8D</scope><scope>JQ2</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140601</creationdate><title>Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications</title><author>Thirugnanam, Kannan ; Ezhil Reena, Joy T. P. ; Singh, Mukesh ; Kumar, Praveen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-51746fb7648fe2f9e187f91918f7d0dac3890c1d963ed0b85aa6965b15c9579b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Batteries</topic><topic>capacity loss</topic><topic>Charge</topic><topic>Circuits</topic><topic>Control algorithms</topic><topic>Discharges (electric)</topic><topic>Electric charge</topic><topic>Electric vehicles</topic><topic>electric vehicles (EVs)</topic><topic>genetic algorithm (GA)</topic><topic>Genetic algorithms</topic><topic>Integrated circuit modeling</topic><topic>Lithium-ion batteries</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Polynomials</topic><topic>vehicle-to-grid (V2G)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thirugnanam, Kannan</creatorcontrib><creatorcontrib>Ezhil Reena, Joy T. P.</creatorcontrib><creatorcontrib>Singh, Mukesh</creatorcontrib><creatorcontrib>Kumar, Praveen</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>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on energy conversion</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Thirugnanam, Kannan</au><au>Ezhil Reena, Joy T. P.</au><au>Singh, Mukesh</au><au>Kumar, Praveen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications</atitle><jtitle>IEEE transactions on energy conversion</jtitle><stitle>TEC</stitle><date>2014-06-01</date><risdate>2014</risdate><volume>29</volume><issue>2</issue><spage>332</spage><epage>343</epage><pages>332-343</pages><issn>0885-8969</issn><eissn>1558-0059</eissn><coden>ITCNE4</coden><abstract>This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TEC.2014.2298460</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0885-8969 |
| ispartof | IEEE transactions on energy conversion, 2014-06, Vol.29 (2), p.332-343 |
| issn | 0885-8969 1558-0059 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TEC_2014_2298460 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Batteries capacity loss Charge Circuits Control algorithms Discharges (electric) Electric charge Electric vehicles electric vehicles (EVs) genetic algorithm (GA) Genetic algorithms Integrated circuit modeling Lithium-ion batteries Mathematical model Mathematical models Polynomials vehicle-to-grid (V2G) |
| title | Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T18%3A08%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mathematical%20Modeling%20of%20Li-Ion%20Battery%20Using%20Genetic%20Algorithm%20Approach%20for%20V2G%20Applications&rft.jtitle=IEEE%20transactions%20on%20energy%20conversion&rft.au=Thirugnanam,%20Kannan&rft.date=2014-06-01&rft.volume=29&rft.issue=2&rft.spage=332&rft.epage=343&rft.pages=332-343&rft.issn=0885-8969&rft.eissn=1558-0059&rft.coden=ITCNE4&rft_id=info:doi/10.1109/TEC.2014.2298460&rft_dat=%3Cproquest_RIE%3E3377660681%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1545986476&rft_id=info:pmid/&rft_ieee_id=6716979&rfr_iscdi=true |