Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness
Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that shoul...
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| Veröffentlicht in: | Applied composite materials 2024-10, Vol.31 (5), p.1475-1493 |
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| creator | Shaikh, Shadab Anwar Taufique, M. F. N. Balusu, Kranthi Kulkarni, Shank S. Hale, Forrest Oleson, Jonathan Devanathan, Ram Soulami, Ayoub |
| description | Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. In this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R
2
> 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies. |
| doi_str_mv | 10.1007/s10443-024-10218-z |
| format | Article |
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2
> 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.</description><identifier>ISSN: 0929-189X</identifier><identifier>EISSN: 1573-4897</identifier><identifier>DOI: 10.1007/s10443-024-10218-z</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>battery-enclosure ; Carbon fibers ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Classical Mechanics ; composite material ; Corrosion resistance ; Crashworthiness ; Crush tests ; Design analysis ; Electric vehicles ; Enclosures ; Fiber composites ; Finite element analysis ; Finite element method ; Impact strength ; Industrial Chemistry/Chemical Engineering ; Machine learning ; Materials Science ; Performance prediction ; Polymer Sciences ; Prediction models ; Process parameters ; Strength to weight ratio ; symbolic regression ; Thermoforming</subject><ispartof>Applied composite materials, 2024-10, Vol.31 (5), p.1475-1493</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2024. corrected publication 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c297t-d249988f814972121558256e301a638c7fe6c6db39b1b1dc7ebc6b656d9d9fd43</cites><orcidid>0000000212978300 ; 0000000181254237</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10443-024-10218-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10443-024-10218-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/2468646$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Shaikh, Shadab Anwar</creatorcontrib><creatorcontrib>Taufique, M. F. N.</creatorcontrib><creatorcontrib>Balusu, Kranthi</creatorcontrib><creatorcontrib>Kulkarni, Shank S.</creatorcontrib><creatorcontrib>Hale, Forrest</creatorcontrib><creatorcontrib>Oleson, Jonathan</creatorcontrib><creatorcontrib>Devanathan, Ram</creatorcontrib><creatorcontrib>Soulami, Ayoub</creatorcontrib><creatorcontrib>Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)</creatorcontrib><title>Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness</title><title>Applied composite materials</title><addtitle>Appl Compos Mater</addtitle><description>Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. In this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R
2
> 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.</description><subject>battery-enclosure</subject><subject>Carbon fibers</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Classical Mechanics</subject><subject>composite material</subject><subject>Corrosion resistance</subject><subject>Crashworthiness</subject><subject>Crush tests</subject><subject>Design analysis</subject><subject>Electric vehicles</subject><subject>Enclosures</subject><subject>Fiber composites</subject><subject>Finite element analysis</subject><subject>Finite element method</subject><subject>Impact strength</subject><subject>Industrial Chemistry/Chemical Engineering</subject><subject>Machine learning</subject><subject>Materials Science</subject><subject>Performance prediction</subject><subject>Polymer Sciences</subject><subject>Prediction models</subject><subject>Process parameters</subject><subject>Strength to weight ratio</subject><subject>symbolic regression</subject><subject>Thermoforming</subject><issn>0929-189X</issn><issn>1573-4897</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kU1vEzEURS0EEqHwB1hZsB6wPR5_LNuQFKSgbkBiZ3nsN4mr1C5-jqp0yx9nyiCxY_U2517pvkPIW84-cMb0R-RMyr5jQnacCW66x2dkxQfdd9JY_ZysmBW248b-eEleId4yxoxWekV-bVNODejmCHeQG73M_njGhNTnSL_6cEgZ6A58zSnv6fUpRYj0E2DaZ1omuvZ1LJlu0wiV3tS9zwUPAK278jiDV741qGe6yeFY8FQB6VQqXVePh4dS21M74mvyYvJHhDd_7wX5vt18W3_udjfXX9aXuy4Iq1sXhbTWmMlwabXggg-DEYOCnnGvehP0BCqoOPZ25COPQcMY1KgGFW20U5T9BXm39BZsyWGYd4dDKDlDaE5IZZRUM_R-ge5r-XkCbO62nOr8FXQ9Z7rnkisxU2KhQi2IFSZ3X9Odr2fHmXsy4hYjbjbi_hhxj3OoX0I4w3kP9V_1f1K_Ac_bj7w</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Shaikh, Shadab Anwar</creator><creator>Taufique, M. F. N.</creator><creator>Balusu, Kranthi</creator><creator>Kulkarni, Shank S.</creator><creator>Hale, Forrest</creator><creator>Oleson, Jonathan</creator><creator>Devanathan, Ram</creator><creator>Soulami, Ayoub</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000000212978300</orcidid><orcidid>https://orcid.org/0000000181254237</orcidid></search><sort><creationdate>20241001</creationdate><title>Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness</title><author>Shaikh, Shadab Anwar ; Taufique, M. F. 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F. N.</creatorcontrib><creatorcontrib>Balusu, Kranthi</creatorcontrib><creatorcontrib>Kulkarni, Shank S.</creatorcontrib><creatorcontrib>Hale, Forrest</creatorcontrib><creatorcontrib>Oleson, Jonathan</creatorcontrib><creatorcontrib>Devanathan, Ram</creatorcontrib><creatorcontrib>Soulami, Ayoub</creatorcontrib><creatorcontrib>Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>OSTI.GOV</collection><jtitle>Applied composite materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shaikh, Shadab Anwar</au><au>Taufique, M. F. 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| subjects | battery-enclosure Carbon fibers Characterization and Evaluation of Materials Chemistry and Materials Science Classical Mechanics composite material Corrosion resistance Crashworthiness Crush tests Design analysis Electric vehicles Enclosures Fiber composites Finite element analysis Finite element method Impact strength Industrial Chemistry/Chemical Engineering Machine learning Materials Science Performance prediction Polymer Sciences Prediction models Process parameters Strength to weight ratio symbolic regression Thermoforming |
| title | Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness |
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