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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2019 - 1
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 HIGHLY CITED 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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (482 KB) | Citation | Downloads: 803 | Views: 1,215

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

[1] O. Erdinc, B. Vural, M. Uzunoglu, "A dynamic lithium-ion battery model considering the effects of temperature and capacity fading," 2009 International Conference on Clean Electrical Power, Capri, 2009, pp. 383-386.
[CrossRef] [SCOPUS Times Cited 179]


[2] Languang Lu, Xuebing Han, Jianqiu Li, Jianfeng Hua, Minggao Ouyangl, "A review on the key issues for lithium-ion battery management in electric vehicles," Journal of Power Sources, vol. 226, pp. 272-288, Mar. 2013.
[CrossRef]


[3] R. Dedryvere, et al, "Electrode/Electrolyte interface reactivity in high-voltage spinel LiMn1.6Ni0.4O4/Li4Ti5O12 lithium-ion battery," The Journal of Physical Chemistry C, vol. 114, pp. 10999-11008, May. 2010.
[CrossRef] [Web of Science Times Cited 216] [SCOPUS Times Cited 216]


[4] Bin Wang, Jun Xu, Binggang Cao, Xuan Zhou, "A novel multimode hybrid energy storage system and its energy management strategy for electric vehicles," Journal of Power Sources, vol. 281, pp. 432-443, 1. May. 2015.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 65]


[5] CHEN Y., MA Y., CHEN H., "State of charge and state of health estimation for lithium-ion battery through dual sliding mode observer based on amesim-simulink co-simulation," Journal of Renewable and Sustainable Energy, vol. 10, pp. 034103, Jun. 2018.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[6] Changfu Zou, Chris Manzie, Dragan N, Abhijit G.Kallapur, "Multi-Time-Scale observer design for state-of-charge and state-of-health of a lithium-ion battery," Journal of Power Sources, vol. 335, pp. 121-130, Dec. 2016.
[CrossRef] [Web of Science Times Cited 114] [SCOPUS Times Cited 120]


[7] D. O. Feder, M. J. Hlavac, "Analysis and interpretation of conductance measurements used to assess the state-of-health of valve regulated lead acid batteries," Proceedings of Intelec 94, International Telecommunications Energy Conference, Vancouver, 1994, pp. 282-291.
[CrossRef]


[8] He Y., Liu X., Zhang C., Chen Z., "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, vol. 101, pp. 808-814, Jan. 2013.
[CrossRef] [Web of Science Times Cited 162] [SCOPUS Times Cited 181]


[9] Pan H., Lu Z., Lin W., Li J., Chen L., "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, vol. 138, pp. 764-75, Nov. 2017.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 42]


[10] Shi W., Hu X., Wang J., Jiang J., Zhang Y., Yip T., "Analysis of thermal aging paths for large-format LiFePO4/graphite battery," Electrochimica Acta, vol. 196, pp. 13-23, Apr. 2016.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 15]


[11] Yang Y., Hu X., Pei H., Peng Z., "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: dynamic programming approach," Applied Energy, vol. 168, pp. 683-690, Apr. 2016.
[CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 106]


[12] Chen L., Lin W., Li J., Tian B., Pan H., "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, vol.106, pp. 662-672, Jul. 2016.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 28]


[13] Waag, Wladislaw, C. Fleischer, D. U. Sauer, "Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles," Journal of Power Sources, vol. 258, pp. 321-339, Jul. 2014.
[CrossRef]


[14] J. H. Aylor, A. Thieme, B. W. Johnso, "A battery state-of-charge indicator for electric wheelchairs," IEEE Transactions on Industrial Electronics, vol. 39, pp. 398-409, Oct. 1992.
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 179]


[15] Roscher MA, Sauer DU, "Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries," Journal of Power Sources, vol. 196, pp. 331-336, Jan.2011.
[CrossRef] [Web of Science Times Cited 189] [SCOPUS Times Cited 223]


[16] Chiang Y.H., Sean W.Y., Ke J.C., "Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles," Journal of Power Sources, vol. 196, pp. 3921-3932, Apr. 2011.
[CrossRef] [Web of Science Times Cited 202]


[17] Charkhgard M., Farrokhi M., "State-of-charge estimation for lithium-ion batteries using neural networks and EKF," IEEE Transactions on Industrial Electronics, vol. 57, pp. 4178-4187, Dec. 2010.
[CrossRef] [Web of Science Times Cited 400] [SCOPUS Times Cited 487]


[18] Miyamoto, Hiroyuki, M. Morimoto, K. Morita, "Online SOC estimation of battery for wireless tramcar," Electrical Engineering in Japan, vol. 186, pp. 83-89, Jan. 2014.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[19] Li Z., Huang J., Liaw B.Y., Zhang J., "On state-of-charge determination for lithium-ion batteries," Journal of Power Sources, vol. 348, pp. 281-301, Apr. 2017.
[CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 101]


[20] Piller, Sabine, M. Perrin, A. Jossen, "Methods for state-of-charge determination and their applications," Journal of Power Sources, vol. 96, pp. 113-120, Jun. 2001.
[CrossRef] [Web of Science Times Cited 647] [SCOPUS Times Cited 841]


[21] Wei Z., Zhao J., Skyllas-Kazacos M., Xiong B., "Dynamic thermal-hydraulic modeling and stack flow pattern analysis for all-vanadium redox flow battery," Journal of Power Sources, vol. 260, pp. 89-99, Aug. 2014.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 46]


[22] Wei Z., Zhao J., Xiong B., "Dynamic electro-thermal modeling of all-vanadium redox flow battery with forced cooling strategies," Applied Energy, vol. 135, pp. 1-10, Dec. 2014.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 35]


[23] Hu X., Li S., Peng H., "A comparative study of equivalent circuit models for Li-ion batteries," Journal of Power Sources, vol.198, pp. 359-367, Jan. 2012.
[CrossRef] [Web of Science Times Cited 775] [SCOPUS Times Cited 915]


[24] Meng J., Luo G., Gao F., "Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman Filter and support vector machine," IEEE Transactions on Power Electronics, vol. 31, pp. 2226-2238, Mar. 2016.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 115]


[25] Wang Y., Zhang C., Chen Z., "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, vol. 137, pp. 427-434, Jan. 2015.
[CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 75]


[26] Pérez G., Garmendia M., Reynaud J.F., Crego J., Viscarret U., "Enhanced closed loop state of charge estimator for lithium-ion batteries based on Extended Kalman Filter," Applied Energy, vol. 155, pp. 834-845, Oct. 2015.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 64]


[27] He H., Xiong R., Peng J., "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, vol. 162, pp. 1410-1418, Jan. 2016.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 63]


[28] Lim K., Bastawrous H.A., Duong V.-H., See K.W., Zhang P., Dou S.X., "Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, vol. 169, pp. 40-48, May. 2016.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 66]


[29] Wang Y, Zhang C, Chen Z, "A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter," Journal of Power Sources, vol. 279, pp. 306-311, Apr. 2015.
[CrossRef] [Web of Science Times Cited 125] [SCOPUS Times Cited 131]


[30] Wang Y., Zhang C., Chen Z., "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, vol. 135, pp. 81-87, Dec. 2014.
[CrossRef] [Web of Science Times Cited 113] [SCOPUS Times Cited 124]


[31] Lin C., Mu H., Xiong R., Shen W., "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, vol.166, pp. 76-83, Mar. 2016.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 99]


[32] Chen X., Shen W., Cao Z., Kapoor A., "A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles," Journal of Power Sources, vol. 246, pp. 667-678, Jan. 2014.
[CrossRef] [Web of Science Times Cited 119] [SCOPUS Times Cited 130]


[33] Du J., Liu Z., Wang Y., Wen C., "An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles," Control Engineering Practice, vol. 54, pp. 81-90, Sep. 2016.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 49]


[34] G.L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. state parameter estimation," Journal of Power Sources, vol. 134, pp. 277-292, Aug. 2004.
[CrossRef] [Web of Science Times Cited 942] [SCOPUS Times Cited 1104]


[35] Chiang Y.H., Sean W.Y., Ke J.C., "Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles," Journal of Power Sources, vol. 196. pp. 3921-3932, Apr. 2011.
[CrossRef] [Web of Science Times Cited 202] [SCOPUS Times Cited 232]


[36] Charkhgard M., Farrokhi M., "State-of-charge estimation for lithium-ion batteries using neural networks and EKF," IEEE Transactions on Industrial Electronics, vol. 57, pp. 4178-4187, Feb. 2010.
[CrossRef] [Web of Science Times Cited 400] [SCOPUS Times Cited 487]


[37] Deng Z., Yang L., Cai Y., Deng H., Sun L., "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, vol. 112, pp. 469-480, Oct. 2016.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 48]


[38] Alvin J. Salkind, Craig Fennie, Pritpal Singh, Terrill Atwater, David E Reisner "Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology," Journal of Power Sources, vol. 80, pp. 293-300, Jul. 1999.
[CrossRef] [Web of Science Times Cited 240] [SCOPUS Times Cited 309]


[39] Malkhandi S., "Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery," Engineering Application of Artificial Intelligence, vol. 19, pp. 479-485, Aug. 2006.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 76]


[40] Sheng H., Xiao J., "Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine," Journal of Power Sources, vol. 281, pp. 131-137, May. 2015.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 85]


[41] Hussein A.A., "Derivation and comparison of open-loop and closed-loop neural network battery state-of-charge estimators," 7th International Conference on Applied Energy (ICAE), Abu Dhabi, 2015, pp. 1856-1861.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 19]


[42] Zou Y., Hu X., Ma H., Li S.E., "Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles," Journal of Power Sources, vol. 273, pp. 793-803, Jan. 2015.
[CrossRef] [Web of Science Times Cited 240] [SCOPUS Times Cited 279]


[43] Haihong Pan, Zhiqiang Lü, Weilong Li, Junzi Li, LinChen, "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, vol. 138, pp. 764-775, Nov. 2017.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 42]


[44] N. Watrin, B. Blunier, A. Miraoui, "Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation," 2012 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, 2009, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 79]


[45] Xiong R., Gong X., Mi C.C., Sun F., "A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter," Journal of Power Sources, vol. 64, pp. 805-816, Dec. 2013.
[CrossRef] [Web of Science Times Cited 99] [SCOPUS Times Cited 116]


[46] Ramadesigan V., Chen K., Burns N. A., et al, "Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models," Journal of the Electrochemical Society, vol. 158, pp. A1048-A1054, Jul. 2011.
[CrossRef] [Web of Science Times Cited 84] [SCOPUS Times Cited 94]


[47] Hu X., Li S.E., Jia Z., Egardt B., "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, vol. 64, pp. 953-960, Jan. 2014.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 104]


[48] Hu C., Youn B.D., Chung J., "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, vol. 92, pp. 694-704, Apr. 2012.
[CrossRef] [Web of Science Times Cited 284] [SCOPUS Times Cited 326]


[49] Wei He, Nicholas Williard, Chaochao Chen, Michael Pecht, "State of charge estimation for Li-Ion batteries using neural network modeling and unscented Kalman filter-based error cancellation," International Journal of Electrical Power & Energy Systems, vol. 62, pp. 783-791, Nov. 2014.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 171]


[50] He W., Williard, N., Chen, C. C., Pecht M., "State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation," International Journal of Electrical Power & Energy Systems, vol. 62, pp. 783-791, Nov. 2014.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 171]




References Weight

Web of Science® Citations for all references: 7,123 TCR
SCOPUS® Citations for all references: 8,445 TCR

Web of Science® Average Citations per reference: 140 ACR
SCOPUS® Average Citations per reference: 166 ACR

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

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-09-29 17:07 in 452 seconds.




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