000107451 001__ 107451
000107451 005__ 20240122154814.0
000107451 0247_ $$2doi$$a10.3390/electronics10182294
000107451 0248_ $$2sideral$$a124882
000107451 037__ $$aART-2021-124882
000107451 041__ $$aeng
000107451 100__ $$0(orcid)0000-0002-7897-3596$$aPastor-Flores, Pablo
000107451 245__ $$aUnsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries
000107451 260__ $$c2021
000107451 5060_ $$aAccess copy available to the general public$$fUnrestricted
000107451 5203_ $$aThis paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application.
000107451 536__ $$9info:eu-repo/grantAgreement/ES/FEDER/CDTI/MIR-20201042 CARDHIN$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2017-88841-R
000107451 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000107451 590__ $$a2.69$$b2021
000107451 592__ $$a0.59$$b2021
000107451 594__ $$a3.7$$b2021
000107451 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b100 / 163 = 0.613$$c2021$$dQ3$$eT2
000107451 593__ $$aComputer Networks and Communications$$c2021$$dQ2
000107451 591__ $$aPHYSICS, APPLIED$$b82 / 161 = 0.509$$c2021$$dQ3$$eT2
000107451 593__ $$aSignal Processing$$c2021$$dQ2
000107451 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b139 / 274 = 0.507$$c2021$$dQ3$$eT2
000107451 593__ $$aHardware and Architecture$$c2021$$dQ2
000107451 593__ $$aControl and Systems Engineering$$c2021$$dQ2
000107451 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000107451 700__ $$0(orcid)0000-0002-3643-2847$$aMartín-del-Brío, Bonifacio$$uUniversidad de Zaragoza
000107451 700__ $$0(orcid)0000-0001-5664-7063$$aBono-Nuez, Antonio$$uUniversidad de Zaragoza
000107451 700__ $$0(orcid)0000-0003-0198-5094$$aSanz-Gorrachategui, Iván$$uUniversidad de Zaragoza
000107451 700__ $$0(orcid)0000-0001-9334-4870$$aBernal-Ruiz, Carlos$$uUniversidad de Zaragoza
000107451 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000107451 773__ $$g10, 18 (2021), 2294 [20 pp]$$pElectronics (Basel)$$tElectronics$$x2079-9292
000107451 8564_ $$s3584565$$uhttps://zaguan.unizar.es/record/107451/files/texto_completo.pdf$$yVersión publicada
000107451 8564_ $$s2766126$$uhttps://zaguan.unizar.es/record/107451/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000107451 909CO $$ooai:zaguan.unizar.es:107451$$particulos$$pdriver
000107451 951__ $$a2024-01-22-15:33:21
000107451 980__ $$aARTICLE