Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries

Pastor-Flores, Pablo ; Martín-del-Brío, Bonifacio (Universidad de Zaragoza) ; Bono-Nuez, Antonio (Universidad de Zaragoza) ; Sanz-Gorrachategui, Iván (Universidad de Zaragoza) ; Bernal-Ruiz, Carlos (Universidad de Zaragoza)

Resumen: This 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.

Idioma: Inglés

DOI: 10.3390/electronics10182294

Año: 2021

Publicado en: Electronics 10, 18 (2021), 2294 [20 pp]
ISSN: 2079-9292

Factor impacto JCR: 2.69 (2021)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 100 / 163 = 0.613 (2021) - Q3 - T2
Categ. JCR: PHYSICS, APPLIED rank: 82 / 161 = 0.509 (2021) - Q3 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 139 / 274 = 0.507 (2021) - Q3 - T2

Factor impacto CITESCORE: 3.7 - Computer Science (Q2) - Engineering (Q2)

Factor impacto SCIMAGO: 0.59 - Computer Networks and Communications (Q2) - Signal Processing (Q2) - Hardware and Architecture (Q2) - Control and Systems Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/FEDER/CDTI/MIR-20201042 CARDHIN
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2017-88841-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)