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dc.contributor.authorLucu Oyhagaray, Mattin
dc.contributor.authorMartínez Laserna, E.
dc.contributor.authorGandiaga, I.
dc.contributor.authorLiuc, K.
dc.contributor.authorCamblong Ruiz, Aritza ORCID
dc.contributor.authorWidanage, W.D.
dc.contributor.authorMarco, J.
dc.date.accessioned2020-12-22T12:31:06Z
dc.date.available2020-12-22T12:31:06Z
dc.date.issued2020-08
dc.identifier.citationJournal of Energy Storage 30 : (2020) // Article ID 101410es_ES
dc.identifier.issn2352-152X
dc.identifier.urihttp://hdl.handle.net/10810/49224
dc.description.abstractConventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.es_ES
dc.description.sponsorshipThis investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/608936es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectli-ion batteryes_ES
dc.subjectmachine learninges_ES
dc.subjectdata-driven modeles_ES
dc.subjectstate of healthes_ES
dc.subjectremaining useful lifees_ES
dc.subjectgaussian process regressiones_ES
dc.subjectlithium-iones_ES
dc.subjecthealth estimationes_ES
dc.subjectstatees_ES
dc.subjectpredictiones_ES
dc.subjectdegradationes_ES
dc.subjectcombinationes_ES
dc.subjectcalendares_ES
dc.titleData-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data - Part B: Cycling operationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2352152X19314239?via%3Dihubes_ES
dc.identifier.doi10.1016/j.est.2020.101410
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)
Except where otherwise noted, this item's license is described as 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)