Electrochemical Impedance Spectrum Equivalent Circuit Parameter Identification Using a Deep Learning Technique
dc.contributor.author | Zulueta Barbadillo, Asier | |
dc.contributor.author | Zulueta Guerrero, Ekaitz | |
dc.contributor.author | Olarte, Javier | |
dc.contributor.author | Fernández Gámiz, Unai | |
dc.contributor.author | López Guede, José Manuel | |
dc.contributor.author | Etxeberria, Saioa | |
dc.date.accessioned | 2024-01-22T19:02:47Z | |
dc.date.available | 2024-01-22T19:02:47Z | |
dc.date.issued | 2023-12-18 | |
dc.identifier.citation | Electronics 12(24) : (2023) // Article ID 5038 | es_ES |
dc.identifier.issn | 2079-9292 | |
dc.identifier.uri | http://hdl.handle.net/10810/64223 | |
dc.description.abstract | Physical models are suitable for the development and optimization of materials and cell designs, whereas models based on experimental data and electrical equivalent circuits (EECs) are suitable for the development of operation estimators, both for cells and batteries. This research work develops an innovative unsupervised artificial neural network (ANN) training cost function for identifying equivalent circuit parameters using electrochemical impedance spectroscopy (EIS) to identify and monitor parameter variations associated with different physicochemical processes that can be related to the states or failure modes in batteries. Many techniques and algorithms are used to fit a predefined EEC parameter, many requiring high-human-expertise support work. However, once the appropriate EEC model is selected to model the different physicochemical processes associated with a given battery technology, the challenge is to implement algorithms that can automatically calculate parameter variations in real time to allow the implementation of estimators of capacity, health, safety, and other degradation modes. Based on previous studies using data augmentation techniques, the new ANN deep learning method introduced in this study yields better results than classical training algorithms. The data used in this work are based on an aging and characterization dataset for 80 Ah and 12 V lead–acid batteries. | es_ES |
dc.description.sponsorship | The authors were supported by the Mobility Lab Foundation, a governmental organization of the Provincial Council of Araba and the local council of Vitoria-Gasteiz under the project grant of “Control de baterías de flujo”. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | |
dc.subject | neural networks | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | electrical equivalent circuit | es_ES |
dc.subject | electrochemical impedance spectroscopy | es_ES |
dc.subject | model-based estimators | es_ES |
dc.subject | lead–acid batteries | es_ES |
dc.title | Electrochemical Impedance Spectrum Equivalent Circuit Parameter Identification Using a Deep Learning Technique | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2023-12-22T13:46:04Z | |
dc.rights.holder | © 2023 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/2079-9292/12/24/5038 | es_ES |
dc.identifier.doi | 10.3390/electronics12245038 | |
dc.departamentoes | Ingeniería Energética | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Energia Ingenieritza | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as © 2023 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/).