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dc.contributor.author
Avila, Luis Omar
dc.contributor.author
Errecalde, Marcelo Luis
dc.contributor.author
Serra, Federico Martin
dc.contributor.author
Martínez, Ernesto Carlos
dc.date.available
2021-10-23T01:32:34Z
dc.date.issued
2019-10
dc.identifier.citation
Avila, Luis Omar; Errecalde, Marcelo Luis; Serra, Federico Martin; Martínez, Ernesto Carlos; State of charge monitoring of Li-ion batteries for electric vehicles using GP filtering; Elsevier; Journal of Energy Storage; 25; 10-2019; 1-9
dc.identifier.issn
2352-152X
dc.identifier.uri
http://hdl.handle.net/11336/144853
dc.description.abstract
Electric vehicles are dependent on onboard battery management systems that protect the battery from functioning outside its safe operating limits by monitoring its state of charge (SOC). Advanced online monitoring techniques are required so that the performance of the energy management is not lowered severely. However, the behavior of batteries is difficult to be predicted online because of its nonlinearity, intrinsic variability and fluctuating environmental conditions. Gaussian Process (GP)-Bayesian filters are based on probabilistic non-parametric Gaussian models of hidden states using available measurements. As a result, model response variability can be explicitly incorporated into the prediction and measurement steps, which is usually not the case for more traditional filtering strategies that resort to parametric models for state estimation. In this work, GP models were incorporated into nonparametric filtering techniques to monitor the battery SOC online. Results show that Bayes’ filtering techniques increase the predictability of the SOC under uncertainty about the effect of environmental conditions on the SOC.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BATTERY MANAGEMENT SYSTEMS
dc.subject
BATTERY VARIABILITY
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BAYESIAN FILTERING
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GAUSSIAN PROCESSES
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STATE OF CHARGE
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
State of charge monitoring of Li-ion batteries for electric vehicles using GP filtering
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-08-05T16:42:23Z
dc.identifier.eissn
2352-1538
dc.journal.volume
25
dc.journal.pagination
1-9
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.description.fil
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
dc.description.fil
Fil: Serra, Federico Martin. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.description.fil
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
dc.journal.title
Journal of Energy Storage
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2352152X19302373
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.est.2019.100837
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