Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability

Detalhes bibliográficos
Autor(a) principal: Strange, Calum
Data de Publicação: 2022
Outros Autores: Allerhand, Michael, Dechent, Philipp, dos Reis, Gonçalo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150729
Resumo: This project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council (EPSRC) & University of Edinburgh United Kingdom program Impact Acceleration Account (IAA). P. Dechent was supported by Bundesministerium für Bildung und Forschung Germany ( BMBF 03XP0302C ). Publisher Copyright: © 2022 The Author(s)
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spelling Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variabilityBatteryDegradationLithium-ionMachine learningManufacturingStatisticsTestingEngineering (miscellaneous)Energy(all)Artificial IntelligenceThis project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council (EPSRC) & University of Edinburgh United Kingdom program Impact Acceleration Account (IAA). P. Dechent was supported by Bundesministerium für Bildung und Forschung Germany ( BMBF 03XP0302C ). Publisher Copyright: © 2022 The Author(s)The testing of battery cells is a long and expensive process, and hence understanding how large a test set needs to be is very useful. This work proposes an automated methodology to estimate the smallest sample size of cells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variation based on the slopes of a linear regression model applied to capacity fade curves. Our methodology determines a sample size which estimates this variability within user specified requirements on precision and confidence. The sample size is found using the distributional properties of the slopes under a normality assumption, and an implementation of the approach is available on GitHub. For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision and confidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge can be leveraged with machine learning models to operationally optimise the design of new cell-testing, leading up to a 75% reduction in experimental costs.CMA - Centro de Matemática e AplicaçõesRUNStrange, CalumAllerhand, MichaelDechent, Philippdos Reis, Gonçalo2023-03-16T22:38:23Z2022-082022-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9application/pdfhttp://hdl.handle.net/10362/150729engPURE: 56117145https://doi.org/10.1016/j.egyai.2022.100174info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:33:04Zoai:run.unl.pt:10362/150729Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:17.148614Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
title Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
spellingShingle Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
Strange, Calum
Battery
Degradation
Lithium-ion
Machine learning
Manufacturing
Statistics
Testing
Engineering (miscellaneous)
Energy(all)
Artificial Intelligence
title_short Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
title_full Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
title_fullStr Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
title_full_unstemmed Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
title_sort Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
author Strange, Calum
author_facet Strange, Calum
Allerhand, Michael
Dechent, Philipp
dos Reis, Gonçalo
author_role author
author2 Allerhand, Michael
Dechent, Philipp
dos Reis, Gonçalo
author2_role author
author
author
dc.contributor.none.fl_str_mv CMA - Centro de Matemática e Aplicações
RUN
dc.contributor.author.fl_str_mv Strange, Calum
Allerhand, Michael
Dechent, Philipp
dos Reis, Gonçalo
dc.subject.por.fl_str_mv Battery
Degradation
Lithium-ion
Machine learning
Manufacturing
Statistics
Testing
Engineering (miscellaneous)
Energy(all)
Artificial Intelligence
topic Battery
Degradation
Lithium-ion
Machine learning
Manufacturing
Statistics
Testing
Engineering (miscellaneous)
Energy(all)
Artificial Intelligence
description This project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council (EPSRC) & University of Edinburgh United Kingdom program Impact Acceleration Account (IAA). P. Dechent was supported by Bundesministerium für Bildung und Forschung Germany ( BMBF 03XP0302C ). Publisher Copyright: © 2022 The Author(s)
publishDate 2022
dc.date.none.fl_str_mv 2022-08
2022-08-01T00:00:00Z
2023-03-16T22:38:23Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150729
url http://hdl.handle.net/10362/150729
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 56117145
https://doi.org/10.1016/j.egyai.2022.100174
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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