Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Detalhes bibliográficos
Autor(a) principal: Strange, Calum
Data de Publicação: 2023
Outros Autores: Ibraheem, Rasheed, 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/10362/155253
Resumo: This project was funded by an industry-academia grant EPSRC EP/R511687/1 awarded by EPSRC & University of Edinburgh program Impact Acceleration Account (IAA). R. Ibraheem is a Ph.D. student in EPSRC’s MAC-MIGS Centre for Doctoral Training. MAC-MIGS is supported by the UK’s Engineering and Physical Science Research Council (grant number EP/S023291/1). G. dos Reis acknowledges support from the Faraday Institution [grant number FIRG049]. Publisher Copyright: © 2023 by the authors.
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spelling Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensemblingcloud computingensemble modelsmachine learningprediction of full degradation curveremaining-useful-lifeRenewable Energy, Sustainability and the EnvironmentFuel TechnologyEngineering (miscellaneous)Energy Engineering and Power TechnologyEnergy (miscellaneous)Control and OptimizationElectrical and Electronic EngineeringSDG 7 - Affordable and Clean EnergyThis project was funded by an industry-academia grant EPSRC EP/R511687/1 awarded by EPSRC & University of Edinburgh program Impact Acceleration Account (IAA). R. Ibraheem is a Ph.D. student in EPSRC’s MAC-MIGS Centre for Doctoral Training. MAC-MIGS is supported by the UK’s Engineering and Physical Science Research Council (grant number EP/S023291/1). G. dos Reis acknowledges support from the Faraday Institution [grant number FIRG049]. Publisher Copyright: © 2023 by the authors.Lithium-ion batteries have found applications in many parts of our daily lives. Predicting their remaining useful life (RUL) is thus essential for management and prognostics. Most approaches look at early life prediction of RUL in the context of designing charging profiles or optimising cell design. While critical, said approaches are not directly applicable to the regular testing of cells used in applications. This article focuses on a class of models called ‘one-cycle’ models which are suitable for this task and characterized by versatility (in terms of online prediction frameworks and model combinations), prediction from limited input, and cells’ history independence. Our contribution is fourfold. First, we show the wider deployability of the so-called one-cycle model for a different type of battery data, thus confirming its wider scope of use. Second, reflecting on how prediction models can be leveraged within battery management cloud solutions, we propose a universal Exponential-smoothing (e-forgetting) mechanism that leverages cycle-to-cycle prediction updates to reduce prediction variance. Third, we use this new model as a second-life assessment tool by proposing a knee region classifier. Last, using model ensembling, we build a “model of models”. We show that it outperforms each underpinning model (from in-cycle variability, cycle-to-cycle variability, and empirical models). This ‘ensembling’ strategy allows coupling explainable and black-box methods, thus giving the user extra control over the final model.CMA - Centro de Matemática e AplicaçõesRUNStrange, CalumIbraheem, Rasheeddos Reis, Gonçalo2023-07-13T22:18:37Z2023-04-062023-04-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/155253engStrange, C., Ibraheem, R., & dos Reis, G. (2023). Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling. Energies, 16(7), [3273]. https://doi.org/10.3390/en160732731996-1073PURE: 66169186https://doi.org/10.3390/en16073273info: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:RCAAP2024-03-11T05:37:48Zoai:run.unl.pt:10362/155253Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:59.948179Repositório 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 Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
title Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
spellingShingle Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
Strange, Calum
cloud computing
ensemble models
machine learning
prediction of full degradation curve
remaining-useful-life
Renewable Energy, Sustainability and the Environment
Fuel Technology
Engineering (miscellaneous)
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
title_short Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
title_full Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
title_fullStr Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
title_full_unstemmed Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
title_sort Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
author Strange, Calum
author_facet Strange, Calum
Ibraheem, Rasheed
dos Reis, Gonçalo
author_role author
author2 Ibraheem, Rasheed
dos Reis, Gonçalo
author2_role author
author
dc.contributor.none.fl_str_mv CMA - Centro de Matemática e Aplicações
RUN
dc.contributor.author.fl_str_mv Strange, Calum
Ibraheem, Rasheed
dos Reis, Gonçalo
dc.subject.por.fl_str_mv cloud computing
ensemble models
machine learning
prediction of full degradation curve
remaining-useful-life
Renewable Energy, Sustainability and the Environment
Fuel Technology
Engineering (miscellaneous)
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
topic cloud computing
ensemble models
machine learning
prediction of full degradation curve
remaining-useful-life
Renewable Energy, Sustainability and the Environment
Fuel Technology
Engineering (miscellaneous)
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
description This project was funded by an industry-academia grant EPSRC EP/R511687/1 awarded by EPSRC & University of Edinburgh program Impact Acceleration Account (IAA). R. Ibraheem is a Ph.D. student in EPSRC’s MAC-MIGS Centre for Doctoral Training. MAC-MIGS is supported by the UK’s Engineering and Physical Science Research Council (grant number EP/S023291/1). G. dos Reis acknowledges support from the Faraday Institution [grant number FIRG049]. Publisher Copyright: © 2023 by the authors.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-13T22:18:37Z
2023-04-06
2023-04-06T00:00:00Z
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/10362/155253
url http://hdl.handle.net/10362/155253
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Strange, C., Ibraheem, R., & dos Reis, G. (2023). Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling. Energies, 16(7), [3273]. https://doi.org/10.3390/en16073273
1996-1073
PURE: 66169186
https://doi.org/10.3390/en16073273
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
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
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