Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models
The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provi...
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Veröffentlicht in: | Progress in energy 2022-07, Vol.4 (3), p.32004 |
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container_title | Progress in energy |
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creator | Wang, A A O’Kane, S E J Brosa Planella, F Houx, J Le O’Regan, K Zyskin, M Edge, J Monroe, C W Cooper, S J Howey, D A Kendrick, E Foster, J M |
description | The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at
www.liiondb.com
, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature. |
doi_str_mv | 10.1088/2516-1083/ac692c |
format | Article |
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www.liiondb.com
, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.</description><identifier>ISSN: 2516-1083</identifier><identifier>EISSN: 2516-1083</identifier><identifier>DOI: 10.1088/2516-1083/ac692c</identifier><identifier>CODEN: PERNDG</identifier><language>eng</language><publisher>IOP Publishing</publisher><subject>experiment ; lithium-ion battery ; modelling ; Newman model ; parameterisation</subject><ispartof>Progress in energy, 2022-07, Vol.4 (3), p.32004</ispartof><rights>2022 The Author(s). Published by IOP Publishing Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-d2543589f79d4349a8124a643f91f6d55e3dc4171f70eaf3212f81caca8ea8303</citedby><cites>FETCH-LOGICAL-c369t-d2543589f79d4349a8124a643f91f6d55e3dc4171f70eaf3212f81caca8ea8303</cites><orcidid>0000-0003-3141-1657 ; 0000-0002-4219-964X ; 0000-0002-5266-594X ; 0000-0002-0620-3955 ; 0000-0002-9894-5023 ; 0000-0003-4055-6903 ; 0000-0003-1864-5213 ; 0000-0001-6363-2812 ; 0000-0002-1576-0673 ; 0000-0003-4643-2426 ; 0000-0002-6120-5734</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/2516-1083/ac692c/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,27901,27902,53821</link.rule.ids></links><search><creatorcontrib>Wang, A A</creatorcontrib><creatorcontrib>O’Kane, S E J</creatorcontrib><creatorcontrib>Brosa Planella, F</creatorcontrib><creatorcontrib>Houx, J Le</creatorcontrib><creatorcontrib>O’Regan, K</creatorcontrib><creatorcontrib>Zyskin, M</creatorcontrib><creatorcontrib>Edge, J</creatorcontrib><creatorcontrib>Monroe, C W</creatorcontrib><creatorcontrib>Cooper, S J</creatorcontrib><creatorcontrib>Howey, D A</creatorcontrib><creatorcontrib>Kendrick, E</creatorcontrib><creatorcontrib>Foster, J M</creatorcontrib><title>Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models</title><title>Progress in energy</title><addtitle>PRGE</addtitle><addtitle>Prog. Energy</addtitle><description>The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at
www.liiondb.com
, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.</description><subject>experiment</subject><subject>lithium-ion battery</subject><subject>modelling</subject><subject>Newman model</subject><subject>parameterisation</subject><issn>2516-1083</issn><issn>2516-1083</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><recordid>eNp1kEtLw0AUhQdRsNTuXc5Swdh5JZkstdYHBATRpYTbecCUJBNmkkr_vQkRcePqHu693-FwELqk5JYSKdcspVkyKr4GlRVMnaDF7-r0jz5Hqxj3hBDOGOFULNDnmzk484W9xR0EaExvgovQO99iaDUG3PqDqbGGHnYQDb4q3Xh7uL_G1gesfNu7dhgaXLpkYnbQjw5H3Hht6niBzizU0ax-5hJ9PG7fN89J-fr0srkrE8Wzok80SwVPZWHzQgsuCpCUCcgEtwW1mU5Tw7USNKc2JwYsZ5RZSRUokAYkJ3yJyOyrgo8xGFt1wTUQjhUl1dRQNVUwKV7NDY3IzYw431V7P4R2DPj_-zcz82bz</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Wang, A A</creator><creator>O’Kane, S E J</creator><creator>Brosa Planella, F</creator><creator>Houx, J Le</creator><creator>O’Regan, K</creator><creator>Zyskin, M</creator><creator>Edge, J</creator><creator>Monroe, C W</creator><creator>Cooper, S J</creator><creator>Howey, D A</creator><creator>Kendrick, E</creator><creator>Foster, J M</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-3141-1657</orcidid><orcidid>https://orcid.org/0000-0002-4219-964X</orcidid><orcidid>https://orcid.org/0000-0002-5266-594X</orcidid><orcidid>https://orcid.org/0000-0002-0620-3955</orcidid><orcidid>https://orcid.org/0000-0002-9894-5023</orcidid><orcidid>https://orcid.org/0000-0003-4055-6903</orcidid><orcidid>https://orcid.org/0000-0003-1864-5213</orcidid><orcidid>https://orcid.org/0000-0001-6363-2812</orcidid><orcidid>https://orcid.org/0000-0002-1576-0673</orcidid><orcidid>https://orcid.org/0000-0003-4643-2426</orcidid><orcidid>https://orcid.org/0000-0002-6120-5734</orcidid></search><sort><creationdate>20220701</creationdate><title>Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models</title><author>Wang, A A ; O’Kane, S E J ; Brosa Planella, F ; Houx, J Le ; O’Regan, K ; Zyskin, M ; Edge, J ; Monroe, C W ; Cooper, S J ; Howey, D A ; Kendrick, E ; Foster, J M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-d2543589f79d4349a8124a643f91f6d55e3dc4171f70eaf3212f81caca8ea8303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>experiment</topic><topic>lithium-ion battery</topic><topic>modelling</topic><topic>Newman model</topic><topic>parameterisation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, A A</creatorcontrib><creatorcontrib>O’Kane, S E J</creatorcontrib><creatorcontrib>Brosa Planella, F</creatorcontrib><creatorcontrib>Houx, J Le</creatorcontrib><creatorcontrib>O’Regan, K</creatorcontrib><creatorcontrib>Zyskin, M</creatorcontrib><creatorcontrib>Edge, J</creatorcontrib><creatorcontrib>Monroe, C W</creatorcontrib><creatorcontrib>Cooper, S J</creatorcontrib><creatorcontrib>Howey, D A</creatorcontrib><creatorcontrib>Kendrick, E</creatorcontrib><creatorcontrib>Foster, J M</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><jtitle>Progress in energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, A A</au><au>O’Kane, S E J</au><au>Brosa Planella, F</au><au>Houx, J Le</au><au>O’Regan, K</au><au>Zyskin, M</au><au>Edge, J</au><au>Monroe, C W</au><au>Cooper, S J</au><au>Howey, D A</au><au>Kendrick, E</au><au>Foster, J M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models</atitle><jtitle>Progress in energy</jtitle><stitle>PRGE</stitle><addtitle>Prog. Energy</addtitle><date>2022-07-01</date><risdate>2022</risdate><volume>4</volume><issue>3</issue><spage>32004</spage><pages>32004-</pages><issn>2516-1083</issn><eissn>2516-1083</eissn><coden>PERNDG</coden><abstract>The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at
www.liiondb.com
, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.</abstract><pub>IOP Publishing</pub><doi>10.1088/2516-1083/ac692c</doi><tpages>39</tpages><orcidid>https://orcid.org/0000-0003-3141-1657</orcidid><orcidid>https://orcid.org/0000-0002-4219-964X</orcidid><orcidid>https://orcid.org/0000-0002-5266-594X</orcidid><orcidid>https://orcid.org/0000-0002-0620-3955</orcidid><orcidid>https://orcid.org/0000-0002-9894-5023</orcidid><orcidid>https://orcid.org/0000-0003-4055-6903</orcidid><orcidid>https://orcid.org/0000-0003-1864-5213</orcidid><orcidid>https://orcid.org/0000-0001-6363-2812</orcidid><orcidid>https://orcid.org/0000-0002-1576-0673</orcidid><orcidid>https://orcid.org/0000-0003-4643-2426</orcidid><orcidid>https://orcid.org/0000-0002-6120-5734</orcidid><oa>free_for_read</oa></addata></record> |
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title | Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models |
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