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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
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WorldCat: 643243560
doi: 10.4316/AECE


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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: 2,011 | Views: 4,272

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.


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Cited-By Clarivate Web of Science

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SCOPUS® Times Cited: 26
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Cited-By CrossRef

[1] A Review on Battery Modelling Techniques, Tamilselvi, S., Gunasundari, S., Karuppiah, N., Razak RK, Abdul, Madhusudan, S., Nagarajan, Vikas Madhav, Sathish, T., Shamim, Mohammed Zubair M., Saleel, C. Ahamed, Afzal, Asif, Sustainability, ISSN 2071-1050, Issue 18, Volume 13, 2021.
Digital Object Identifier: 10.3390/su131810042
[CrossRef]

[2] 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]

[3] An improved grey wolf optimization–double adaptive extended Kalman filtering algorithm for co-estimation of state of charge and state of health for lithium-ion batteries based on temperature-dependent second-order RC model, Chen, Lu, Wang, Shunli, Chen, Lei, Gao, Haiying, Fernandez, Carlos, Ionics, ISSN 0947-7047, 2024.
Digital Object Identifier: 10.1007/s11581-024-05610-5
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[4] Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis, Hernández, Jordy Alexander, Fernández, Efrén, Torres, Hugo, World Electric Vehicle Journal, ISSN 2032-6653, Issue 11, Volume 14, 2023.
Digital Object Identifier: 10.3390/wevj14110312
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[5] A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries, Liu, Donglei, Wang, Shunli, Fan, Yongcun, Xia, Lili, Qiu, Jingsong, International Journal of Circuit Theory and Applications, ISSN 0098-9886, Issue 11, Volume 50, 2022.
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[6] Hydrostatic pressure adaptive dual-polarized model for state of charge estimation of lithium-ion batteries, Li, Mengjie, Hu, Yuli, Mao, Zhaoyong, Chen, Peiyu, Zeng, Liteng, Lu, Chengyi, Journal of Energy Storage, ISSN 2352-152X, Issue , 2023.
Digital Object Identifier: 10.1016/j.est.2023.108979
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Digital Object Identifier: 10.1016/j.est.2024.111089
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[8] 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
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[9] Online parameters identification and state of charge estimation for lithium‐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
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[10] 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
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[11] 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
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[12] 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.
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[13] 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
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[14] Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter, Nian, Peng, Shuzhi, Zhang, Xiongwen, Zhang, Journal of Energy Storage, ISSN 2352-152X, Issue , 2021.
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[15] The State of Charge Estimation of LiFePO4 Batteries Performance Using Feed Forward Neural Network Model, Dyartanti, Endah Retno, Jamaluddin, Anif, Akshya, Muhammad Farrel, Akhir, Dimas Zuda Fathul, Gustiana, Himmah Sekar Eka Ayu, Purwanto, Agus, Abharan, Aficena Himdani Ilmam, Nizam, Muhammad, Applied Mechanics and Materials, ISSN 1662-7482, Issue , 2024.
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[16] 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.
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[17] Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery, Qi, Nan, Yan, Kang, Yu, Yajuan, Li, Rui, Huang, Rong, Chen, Lai, Su, Yuefeng, Frontiers in Energy, ISSN 2095-1701, Issue 2, Volume 18, 2024.
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[18] Improved Long Short-Term Memory: Statistical Regression Model for High Precision SOC Estimation of Lithium-Ion Batteries Adaptive to Complex Current Variation Conditions, Wang, Zhi, Wang, Shunli, Yu, Chunmei, Qiao, Jialu, Journal of The Electrochemical Society, ISSN 0013-4651, Issue 5, Volume 170, 2023.
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[19] 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.
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[20] A novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method for accurate state‐of‐charge and state‐of‐health co‐estimation of lithium‐ion batteries, Qiao, Jialu, Wang, Shunli, Yu, Chunmei, Shi, Weihao, Fernandez, Carlos, International Journal of Circuit Theory and Applications, ISSN 0098-9886, Issue 11, Volume 49, 2021.
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[21] A novel Drosophila‐back propagation method for the lithium‐ion battery state of charge estimation adaptive to complex working conditions, Xu, Hong, Wang, Shunli, Fan, Yongcun, Qiao, Jialu, Xu, Wenhua, International Journal of Energy Research, ISSN 0363-907X, Issue 11, Volume 46, 2022.
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[22] State Of Charge Estimation of Lithium Battery Based on Exogenous Kalman Filter, Liu, Yu, Xiang, Jiawei, Shao, Liang, Song, Chenqi, 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), ISBN 978-1-6654-0267-5, 2021.
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[24] IoT Based Battery Monitoring System for Electric Vehicle using ESP32, Rani, R. Usha, Divya, G., Swarupa, M. Lakshmi, Navaneetha, Kandle, 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon), ISBN 979-8-3503-0541-8, 2023.
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[25] Performance of different Electric vehicle Battery packs at low temperature and Analysis of Intelligent SOC experiment, Gao, Le, Cai, Li, Feng, Yuming, Dai, Nina, Xu, Qingshan, 2021 13th International Conference on Advanced Computational Intelligence (ICACI), ISBN 978-1-6654-1254-4, 2021.
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Updated 2 days, 15 hours ago

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