Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy
dc.contributor.author | Olarte, Javier | |
dc.contributor.author | Martínez de Ilarduya Martínez de San Vicente, Jaione | |
dc.contributor.author | Zulueta Guerrero, Ekaitz | |
dc.contributor.author | Ferret, Raquel | |
dc.contributor.author | García Ortega, Joseba | |
dc.contributor.author | López Guede, José Manuel | |
dc.date.accessioned | 2022-11-25T18:49:38Z | |
dc.date.available | 2022-11-25T18:49:38Z | |
dc.date.issued | 2022-11-14 | |
dc.identifier.citation | Batteries 8(11) : (2022) // Article ID 238 | es_ES |
dc.identifier.issn | 2313-0105 | |
dc.identifier.uri | http://hdl.handle.net/10810/58564 | |
dc.description.abstract | This paper introduces the use of a new low-computation cost algorithm combining neural networks with the Nelder–Mead simplex method to monitor the variations of the parameters of a previously selected equivalent circuit calculated from Electrochemical Impedance Spectroscopy (EIS) corresponding to a series of battery aging experiments. These variations could be correlated with variations in the battery state over time and, therefore, identify or predict battery degradation patterns or failure modes. The authors have benchmarked four different Electrical Equivalent Circuit (EEC) parameter identification algorithms: plain neural network mapping EIS raw data to EEC parameters, Particle Swarm Optimization, Zview, and the proposed new one. In order to improve the prediction accuracy of the neural network, a data augmentation method has been proposed to improve the neural network training error. The proposed parameter identification algorithms have been compared and validated through real data obtained from a six-month aging test experiment carried out with a set of six commercial 80 Ah VLRA batteries under different cycling and temperature operation conditions. | es_ES |
dc.description.sponsorship | Special thanks should also be expressed to the Torres Quevedo (PTQ) 2019 Aid from the State Research Agency, within the framework of the State Program for the Promotion of Talent and its Employability in R + D + i, Ref. PTQ2019-010787/AEI/10.13039/501100011033. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PTQ2019-010787 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | lead-acid battery | es_ES |
dc.subject | electrochemical impedance spectroscopy (EIS) | es_ES |
dc.subject | neural networks (NN) | es_ES |
dc.subject | electrical equivalent circuit (EEC) | es_ES |
dc.subject | State of Charge (SOC) | es_ES |
dc.subject | State of Health (SOH) | es_ES |
dc.title | Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-11-24T14:43:23Z | |
dc.rights.holder | © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2313-0105/8/11/238 | es_ES |
dc.identifier.doi | 10.3390/batteries8110238 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).