000145330 001__ 145330
000145330 005__ 20241024135330.0
000145330 0247_ $$2doi$$a10.1109/ACCESS.2024.3474089
000145330 0248_ $$2sideral$$a140111
000145330 037__ $$aART-2024-140111
000145330 041__ $$aeng
000145330 100__ $$0(orcid)0000-0001-5664-7063$$aSanz-Gorrachategui, I.$$uUniversidad de Zaragoza
000145330 245__ $$aRemaining useful life estimation of used li-ion cells with deep learning algorithms without first life information
000145330 260__ $$c2024
000145330 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145330 5203_ $$aThe second life use of lithium-ion batteries has gained significant attention in recent years, driven by the potential to repurpose cells from electric vehicles for less demanding applications. A critical aspect of this repurposing is accurately estimating the Remaining Useful Life (RUL) of the batteries. Traditional techniques often rely on data from the battery’s first life, which may not be available in practical scenarios. To address this issue, we propose a data-driven method for RUL estimation that does not depend on first-life information. Our approach considers a realistic scenario where an aged battery cell, lacking previous usage data, is evaluated for second life use through a limited number of test cycles. We compute features such as incremental capacity curves, and other health indicators from the measured voltage and current waveforms of the used cell. These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This methodology achieves an average error of only 62 cycles for cells with a lifespan of up to 1200 cycles and a RUL error of less than 10% for deeply aged batteries. These results outperform state-of-the-art algorithms that utilize data from the cell’s entire lifespan, demonstrating the efficacy and robustness of this approach.
000145330 536__ $$9info:eu-repo/grantAgreement/ES/DGA/LMP16-18
000145330 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145330 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145330 700__ $$aWang, Y.
000145330 700__ $$aGuillén-Asensio, A.
000145330 700__ $$aBono-Nuez, A.
000145330 700__ $$0(orcid)0000-0002-3643-2847$$aMartin-del-Brio, B.$$uUniversidad de Zaragoza
000145330 700__ $$aOrlik, P. V.
000145330 700__ $$0(orcid)0000-0002-7897-3596$$aPastor-Flores, P.
000145330 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000145330 773__ $$g12 (2024), [11 p.]$$pIEEE Access$$tIEEE Access$$x2169-3536
000145330 8564_ $$s1082885$$uhttps://zaguan.unizar.es/record/145330/files/texto_completo.pdf$$yVersión publicada
000145330 8564_ $$s2549989$$uhttps://zaguan.unizar.es/record/145330/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145330 909CO $$ooai:zaguan.unizar.es:145330$$particulos$$pdriver
000145330 951__ $$a2024-10-24-12:10:31
000145330 980__ $$aARTICLE