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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  3/2019 - 1
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Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation

ZHANG, S. See more information about ZHANG, S. on SCOPUS See more information about ZHANG, S. on IEEExplore See more information about ZHANG, S. on Web of Science, GUO, X. See more information about  GUO, X. on SCOPUS See more information about  GUO, X. on SCOPUS See more information about GUO, X. on Web of Science, ZHANG, X. See more information about ZHANG, X. on SCOPUS See more information about ZHANG, X. on SCOPUS See more information about ZHANG, X. on Web of Science
 
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Download PDF pdficon (482 KB) | Citation | Downloads: 328 | Views: 301

Author keywords
attenuation measurement, backpropagation, battery management systems, lithium batteries, neural networks

References keywords
state(38), charge(30), estimation(28), power(24), lithium(24), battery(24), energy(23), batteries(22), sources(18), jjpowsour(16)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03001
Web of Science Accession Number: 000486574100001
SCOPUS ID: 85072196257

Abstract
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The state of charge of lithium-ion batteries reflects the power available in the battery. Precise SOC estimation is a challenging task for battery management system. In this paper, a novel hybrid method by fusion of back-propagation (BP) neural network and improved ampere-hour counting method is proposed for SOC estimation of lithium-ion battery, which considers the impact of battery capacity attenuation on SOC estimation during the process of charging and discharging. The predictive accuracy and effectiveness of model are validated by NASA lithium-ion battery dataset. Moreover, the adaptability and feasibility of this method are further demonstrated using dataset of accelerated life experiment. The validation results indicate that the proposed method can provide accurate SOC estimation in different capacity attenuation stage.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 6,038 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 118 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

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