000101179 001__ 101179 000101179 005__ 20240122154814.0 000101179 0247_ $$2doi$$a10.1109/IECON.2019.8926907 000101179 0248_ $$2sideral$$a122954 000101179 037__ $$aART-2019-122954 000101179 041__ $$aeng 000101179 100__ $$0(orcid)0000-0002-7897-3596$$aPastor-Flores, P.$$uUniversidad de Zaragoza 000101179 245__ $$aAnalysis of Li-ion battery degradation using self-organizing maps 000101179 260__ $$c2019 000101179 5060_ $$aAccess copy available to the general public$$fUnrestricted 000101179 5203_ $$aThis paper proposes a new methodology to identify the different degradation processes of Li-Ion battery cells. The goal of this study is to determine if different degradation factors can be separated by waveform analysis from aged cells with similar remaining capacity. In contrast to other works, the proposed method identifies the past operating conditions in the cell, regardless of the actual State of Health. The methodology is based on a data-driven approach by using a SOM (Self-organizing map), an unsupervised neural network. To verify the hypothesis a SOM has been trained with laboratory data from whole data cycles, to classify cells concerning their degradation path and according to their discharge voltage patterns. Additionally, this new methodology based on the SOM allows discriminating groups of cells with different cycling conditions (based on depth of discharge, ambient temperature and discharge current). This research line is very promising for classification of used cells, not only depending on their current static parameters (capacity, impedance), but also the battery use in their past life. This will allow making predictions of the Remaining Useful Life (RUL) of a battery with greater precision. 000101179 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/RIS3-LMP16-18$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3358-5 000101179 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000101179 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000101179 700__ $$0(orcid)0000-0001-9334-4870$$aBernal-Ruiz, C.$$uUniversidad de Zaragoza 000101179 700__ $$0(orcid)0000-0003-0198-5094$$aSanz-Gorrachategui, I. 000101179 700__ $$0(orcid)0000-0001-5664-7063$$aBono-Nuez, A.$$uUniversidad de Zaragoza 000101179 700__ $$0(orcid)0000-0002-3643-2847$$aMartin-Del-Brio, B.$$uUniversidad de Zaragoza 000101179 700__ $$0(orcid)0000-0001-7764-235X$$aArtal-Sevil, J.S.$$uUniversidad de Zaragoza 000101179 700__ $$0(orcid)0000-0002-3302-7862$$aPerez-Cebolla, F.J.$$uUniversidad de Zaragoza 000101179 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000101179 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000101179 773__ $$g2019, 10 (2019), 4525-4530$$pProc. Annu. Conf. IEEE Ind. Electron. Soc.$$tProceedings of the Annual Conference of the IEEE Industrial Electronics Society$$x1553-572X 000101179 8564_ $$s1111286$$uhttps://zaguan.unizar.es/record/101179/files/texto_completo.pdf$$yPostprint 000101179 8564_ $$s2945313$$uhttps://zaguan.unizar.es/record/101179/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000101179 909CO $$ooai:zaguan.unizar.es:101179$$particulos$$pdriver 000101179 951__ $$a2024-01-22-15:35:11 000101179 980__ $$aARTICLE