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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: 1,374 | Views: 2,775

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 199]


[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] [Web of Science Times Cited 2728] [SCOPUS Times Cited 3048]


[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 244] [SCOPUS Times Cited 246]


[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 83] [SCOPUS Times Cited 97]


[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 14] [SCOPUS Times Cited 16]


[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 152] [SCOPUS Times Cited 163]


[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 199] [SCOPUS Times Cited 228]


[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 60] [SCOPUS Times Cited 68]


[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 19] [SCOPUS Times Cited 22]


[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 132] [SCOPUS Times Cited 153]


[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 31] [SCOPUS Times Cited 41]


[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] [Web of Science Times Cited 623] [SCOPUS Times Cited 687]


[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 138] [SCOPUS Times Cited 195]


[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 243] [SCOPUS Times Cited 271]


[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 257] [SCOPUS Times Cited 290]


[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 476] [SCOPUS Times Cited 581]


[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 4] [SCOPUS Times Cited 4]


[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 146] [SCOPUS Times Cited 161]


[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 729] [SCOPUS Times Cited 940]


[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 51] [SCOPUS Times Cited 56]


[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 48] [SCOPUS Times Cited 51]


[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 1091] [SCOPUS Times Cited 1264]


[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 177] [SCOPUS Times Cited 213]


[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 79] [SCOPUS Times Cited 92]


[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 72] [SCOPUS Times Cited 75]


[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 79] [SCOPUS Times Cited 91]


[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 86] [SCOPUS Times Cited 99]


[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 163] [SCOPUS Times Cited 176]


[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 147] [SCOPUS Times Cited 157]


[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 122] [SCOPUS Times Cited 131]


[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 141] [SCOPUS Times Cited 157]


[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 72] [SCOPUS Times Cited 78]


[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 1158] [SCOPUS Times Cited 1341]


[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 257] [SCOPUS Times Cited 290]


[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 476] [SCOPUS Times Cited 581]


[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 81] [SCOPUS Times Cited 87]


[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 288] [SCOPUS Times Cited 359]


[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 77] [SCOPUS Times Cited 92]


[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 123] [SCOPUS Times Cited 133]


[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 18] [SCOPUS Times Cited 23]


[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 371] [SCOPUS Times Cited 427]


[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 60] [SCOPUS Times Cited 68]


[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 112]


[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 132] [SCOPUS Times Cited 146]


[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 121] [SCOPUS Times Cited 128]


[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 125] [SCOPUS Times Cited 137]


[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 362] [SCOPUS Times Cited 411]


[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 228] [SCOPUS Times Cited 273]


[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 228] [SCOPUS Times Cited 273]




References Weight

Web of Science® Citations for all references: 12,711 TCR
SCOPUS® Citations for all references: 14,931 TCR

Web of Science® Average Citations per reference: 249 ACR
SCOPUS® Average Citations per reference: 293 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 2022-08-12 03:59 in 320 seconds.




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