A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution
Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the co...
Gespeichert in:
| Veröffentlicht in: | Materials 2022-05, Vol.15 (9), p.3331 |
|---|---|
| Hauptverfasser: | , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 9 |
| container_start_page | 3331 |
| container_title | Materials |
| container_volume | 15 |
| creator | Yang, Dezhen Cui, Yidan Xia, Quan Jiang, Fusheng Ren, Yi Sun, Bo Feng, Qiang Wang, Zili Yang, Chao |
| description | Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively. |
| doi_str_mv | 10.3390/ma15093331 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9103731</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2663066536</sourcerecordid><originalsourceid>FETCH-LOGICAL-c406t-1ae801d530f387efe6453dcc3850eafbfa50c57d0a3e9879e4b78182013d7cab3</originalsourceid><addsrcrecordid>eNpdkU1LJDEQhsOiqLhe_AFLYC-y0LvJVKe7cxFm_ViFET3oOWSS6plId2dM0iP-ezP4sWpdqsj78FKVl5BDzn4DSPan11wwCQD8G9njUlYFl2W59WHeJQcx3rNcGWomcofsghCSV5XYI4spPXULl3RHbx_dUJwGt8aBzlyL9CagdSY5P9ArTEtvqW-zkpZu7IvL_PpXp4TBYcxTxCwPdGr1KmULeuUtdvRs7btx4_CdbLe6i3jw2vfJ3fnZ7clFMbv-d3kynRWmZFUquMaGcSuAtdDU2GJVCrDGQCMY6nbeasGMqC3TgLKpJZbzuslHMQ62NnoO--T4xXc1znu0BocUdKdWwfU6PCmvnfqsDG6pFn6tJGdQA88GR68GwT-MGJPqXTTYdXpAP0Y1qaq6lsAnIqM_v6D3fgxDPm9DAcsfDFWmfr1QJvgYA7bvy3CmNhGq_xFm-MfH9d_Rt8DgGXOvloI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2663066536</pqid></control><display><type>article</type><title>A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution</title><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Yang, Dezhen ; Cui, Yidan ; Xia, Quan ; Jiang, Fusheng ; Ren, Yi ; Sun, Bo ; Feng, Qiang ; Wang, Zili ; Yang, Chao</creator><creatorcontrib>Yang, Dezhen ; Cui, Yidan ; Xia, Quan ; Jiang, Fusheng ; Ren, Yi ; Sun, Bo ; Feng, Qiang ; Wang, Zili ; Yang, Chao</creatorcontrib><description>Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma15093331</identifier><identifier>PMID: 35591665</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Adaptive algorithms ; Aircraft ; Battery cycles ; Degradation ; Digital twins ; Evolution & development ; Evolutionary algorithms ; Fault diagnosis ; Life prediction ; Lithium ; Lithium-ion batteries ; Maintenance costs ; Neural networks ; Parameter estimation ; Predictive maintenance ; Randomness ; Rechargeable batteries ; Reliability analysis ; Simulation ; Visualization</subject><ispartof>Materials, 2022-05, Vol.15 (9), p.3331</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-1ae801d530f387efe6453dcc3850eafbfa50c57d0a3e9879e4b78182013d7cab3</citedby><cites>FETCH-LOGICAL-c406t-1ae801d530f387efe6453dcc3850eafbfa50c57d0a3e9879e4b78182013d7cab3</cites><orcidid>0000-0002-6081-9507 ; 0000-0003-2454-7839 ; 0000-0002-3665-700X ; 0000-0003-3526-296X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103731/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103731/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35591665$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Dezhen</creatorcontrib><creatorcontrib>Cui, Yidan</creatorcontrib><creatorcontrib>Xia, Quan</creatorcontrib><creatorcontrib>Jiang, Fusheng</creatorcontrib><creatorcontrib>Ren, Yi</creatorcontrib><creatorcontrib>Sun, Bo</creatorcontrib><creatorcontrib>Feng, Qiang</creatorcontrib><creatorcontrib>Wang, Zili</creatorcontrib><creatorcontrib>Yang, Chao</creatorcontrib><title>A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution</title><title>Materials</title><addtitle>Materials (Basel)</addtitle><description>Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.</description><subject>Accuracy</subject><subject>Adaptive algorithms</subject><subject>Aircraft</subject><subject>Battery cycles</subject><subject>Degradation</subject><subject>Digital twins</subject><subject>Evolution & development</subject><subject>Evolutionary algorithms</subject><subject>Fault diagnosis</subject><subject>Life prediction</subject><subject>Lithium</subject><subject>Lithium-ion batteries</subject><subject>Maintenance costs</subject><subject>Neural networks</subject><subject>Parameter estimation</subject><subject>Predictive maintenance</subject><subject>Randomness</subject><subject>Rechargeable batteries</subject><subject>Reliability analysis</subject><subject>Simulation</subject><subject>Visualization</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkU1LJDEQhsOiqLhe_AFLYC-y0LvJVKe7cxFm_ViFET3oOWSS6plId2dM0iP-ezP4sWpdqsj78FKVl5BDzn4DSPan11wwCQD8G9njUlYFl2W59WHeJQcx3rNcGWomcofsghCSV5XYI4spPXULl3RHbx_dUJwGt8aBzlyL9CagdSY5P9ArTEtvqW-zkpZu7IvL_PpXp4TBYcxTxCwPdGr1KmULeuUtdvRs7btx4_CdbLe6i3jw2vfJ3fnZ7clFMbv-d3kynRWmZFUquMaGcSuAtdDU2GJVCrDGQCMY6nbeasGMqC3TgLKpJZbzuslHMQ62NnoO--T4xXc1znu0BocUdKdWwfU6PCmvnfqsDG6pFn6tJGdQA88GR68GwT-MGJPqXTTYdXpAP0Y1qaq6lsAnIqM_v6D3fgxDPm9DAcsfDFWmfr1QJvgYA7bvy3CmNhGq_xFm-MfH9d_Rt8DgGXOvloI</recordid><startdate>20220506</startdate><enddate>20220506</enddate><creator>Yang, Dezhen</creator><creator>Cui, Yidan</creator><creator>Xia, Quan</creator><creator>Jiang, Fusheng</creator><creator>Ren, Yi</creator><creator>Sun, Bo</creator><creator>Feng, Qiang</creator><creator>Wang, Zili</creator><creator>Yang, Chao</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6081-9507</orcidid><orcidid>https://orcid.org/0000-0003-2454-7839</orcidid><orcidid>https://orcid.org/0000-0002-3665-700X</orcidid><orcidid>https://orcid.org/0000-0003-3526-296X</orcidid></search><sort><creationdate>20220506</creationdate><title>A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution</title><author>Yang, Dezhen ; Cui, Yidan ; Xia, Quan ; Jiang, Fusheng ; Ren, Yi ; Sun, Bo ; Feng, Qiang ; Wang, Zili ; Yang, Chao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-1ae801d530f387efe6453dcc3850eafbfa50c57d0a3e9879e4b78182013d7cab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Adaptive algorithms</topic><topic>Aircraft</topic><topic>Battery cycles</topic><topic>Degradation</topic><topic>Digital twins</topic><topic>Evolution & development</topic><topic>Evolutionary algorithms</topic><topic>Fault diagnosis</topic><topic>Life prediction</topic><topic>Lithium</topic><topic>Lithium-ion batteries</topic><topic>Maintenance costs</topic><topic>Neural networks</topic><topic>Parameter estimation</topic><topic>Predictive maintenance</topic><topic>Randomness</topic><topic>Rechargeable batteries</topic><topic>Reliability analysis</topic><topic>Simulation</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Dezhen</creatorcontrib><creatorcontrib>Cui, Yidan</creatorcontrib><creatorcontrib>Xia, Quan</creatorcontrib><creatorcontrib>Jiang, Fusheng</creatorcontrib><creatorcontrib>Ren, Yi</creatorcontrib><creatorcontrib>Sun, Bo</creatorcontrib><creatorcontrib>Feng, Qiang</creatorcontrib><creatorcontrib>Wang, Zili</creatorcontrib><creatorcontrib>Yang, Chao</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Dezhen</au><au>Cui, Yidan</au><au>Xia, Quan</au><au>Jiang, Fusheng</au><au>Ren, Yi</au><au>Sun, Bo</au><au>Feng, Qiang</au><au>Wang, Zili</au><au>Yang, Chao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution</atitle><jtitle>Materials</jtitle><addtitle>Materials (Basel)</addtitle><date>2022-05-06</date><risdate>2022</risdate><volume>15</volume><issue>9</issue><spage>3331</spage><pages>3331-</pages><issn>1996-1944</issn><eissn>1996-1944</eissn><abstract>Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35591665</pmid><doi>10.3390/ma15093331</doi><orcidid>https://orcid.org/0000-0002-6081-9507</orcidid><orcidid>https://orcid.org/0000-0003-2454-7839</orcidid><orcidid>https://orcid.org/0000-0002-3665-700X</orcidid><orcidid>https://orcid.org/0000-0003-3526-296X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1996-1944 |
| ispartof | Materials, 2022-05, Vol.15 (9), p.3331 |
| issn | 1996-1944 1996-1944 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9103731 |
| source | PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Adaptive algorithms Aircraft Battery cycles Degradation Digital twins Evolution & development Evolutionary algorithms Fault diagnosis Life prediction Lithium Lithium-ion batteries Maintenance costs Neural networks Parameter estimation Predictive maintenance Randomness Rechargeable batteries Reliability analysis Simulation Visualization |
| title | A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T17%3A54%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Digital%20Twin-Driven%20Life%20Prediction%20Method%20of%20Lithium-Ion%20Batteries%20Based%20on%20Adaptive%20Model%20Evolution&rft.jtitle=Materials&rft.au=Yang,%20Dezhen&rft.date=2022-05-06&rft.volume=15&rft.issue=9&rft.spage=3331&rft.pages=3331-&rft.issn=1996-1944&rft.eissn=1996-1944&rft_id=info:doi/10.3390/ma15093331&rft_dat=%3Cproquest_pubme%3E2663066536%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2663066536&rft_id=info:pmid/35591665&rfr_iscdi=true |