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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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  3/2019 - 1
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 HIGH-IMPACT PAPER 

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: 848 | Views: 1,530

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

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 9 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 8
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Cited-By CrossRef

[1] An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery, Zhang, Shuzhi, Guo, Xu, Zhang, Xiongwen, Journal of Energy Storage, ISSN 2352-152X, Issue , 2020.
Digital Object Identifier: 10.1016/j.est.2020.101980
[CrossRef]

[2] A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis, Zhang, Shuzhi, Guo, Xu, Dou, Xiaoxin, Zhang, Xiongwen, Journal of Power Sources, ISSN 0378-7753, Issue , 2020.
Digital Object Identifier: 10.1016/j.jpowsour.2020.228740
[CrossRef]

[3] A purpose-oriented shuffled complex evolution optimization algorithm for energy management of multi-microgrid systems considering outage duration uncertainty, Bayat, Peyman, Afrakhte, Hossein, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 2, Volume 38, 2020.
Digital Object Identifier: 10.3233/JIFS-190666
[CrossRef]

[4] A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters, Shuzhi, Zhang, Xu, Guo, Xiongwen, Zhang, Journal of Energy Storage, ISSN 2352-152X, Issue , 2021.
Digital Object Identifier: 10.1016/j.est.2020.102093
[CrossRef]

[5] Online parameters identification and state of charge estimation for lithiumā€ion batteries using improved adaptive dual unscented Kalman filter, Peng, Nian, Zhang, Shuzhi, Guo, Xu, Zhang, Xiongwen, International Journal of Energy Research, ISSN 0363-907X, Issue 1, Volume 45, 2021.
Digital Object Identifier: 10.1002/er.6088
[CrossRef]

[6] A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery, Zhang, Shuzhi, Guo, Xu, Dou, Xiaoxin, Zhang, Xiongwen, Sustainable Energy Technologies and Assessments, ISSN 2213-1388, Issue , 2020.
Digital Object Identifier: 10.1016/j.seta.2020.100752
[CrossRef]

[7] Multi-objective decision analysis for data-driven based estimation of battery states: A case study of remaining useful life estimation, Zhang, Shuzhi, Guo, Xu, Zhang, Xiongwen, International Journal of Hydrogen Energy, ISSN 0360-3199, Issue 27, Volume 45, 2020.
Digital Object Identifier: 10.1016/j.ijhydene.2020.03.100
[CrossRef]

[8] Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries, Shi, Haotian, Wang, Shunli, Fernandez, Carlos, Yu, Chunmei, Li, Xiaoxia, Zou, Chuanyun, Measurement and Control, ISSN 0020-2940, Issue 9-10, Volume 53, 2020.
Digital Object Identifier: 10.1177/0020294020923057
[CrossRef]

[9] Nonlinear Modeling of Lithium-Ion Battery Cells for Electric Vehicles using a Hammerstein–Wiener Model, Khalfi, Jaouad, Boumaaz, Najib, Soulmani, Abdallah, Laadissi, El Mehdi, Journal of Electrical Engineering & Technology, ISSN 1975-0102, Issue 2, Volume 16, 2021.
Digital Object Identifier: 10.1007/s42835-020-00607-2
[CrossRef]

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