Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , |
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) |
id |
RCAP_4146d58b5bb68328cee7e2ef41172497 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/150729 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799138131947028480 |