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Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity AttenuationZHANG, S.   , GUO, X. , ZHANG, X. |
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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
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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| [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 212] [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 3039] [SCOPUS Times Cited 3429] [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 252] [SCOPUS Times Cited 257] [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 88] [SCOPUS Times Cited 104] [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 15] [SCOPUS Times Cited 18] [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 164] [SCOPUS Times Cited 178] [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 211] [SCOPUS Times Cited 244] [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 75] [SCOPUS Times Cited 84] [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 21] [SCOPUS Times Cited 25] [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 147] [SCOPUS Times Cited 167] [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 36] [SCOPUS Times Cited 45] [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 685] [SCOPUS Times Cited 758] [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 143] [SCOPUS Times Cited 204] [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 256] [SCOPUS Times Cited 289] [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 271] [SCOPUS Times Cited 303] [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 518] [SCOPUS Times Cited 635] [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 166] [SCOPUS Times Cited 184] [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 751] [SCOPUS Times Cited 972] [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 59] [SCOPUS Times Cited 64] [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 59] [SCOPUS Times Cited 62] [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 1192] [SCOPUS Times Cited 1399] [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 204] [SCOPUS Times Cited 243] [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 83] [SCOPUS Times Cited 96] [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 75] [SCOPUS Times Cited 79] [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 87] [SCOPUS Times Cited 99] [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 92] [SCOPUS Times Cited 106] [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 177] [SCOPUS Times Cited 189] [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 156] [SCOPUS Times Cited 167] [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 145] [SCOPUS Times Cited 157] [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 152] [SCOPUS Times Cited 172] [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 81] [SCOPUS Times Cited 89] [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 1248] [SCOPUS Times Cited 1432] [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 271] [SCOPUS Times Cited 303] [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 518] [SCOPUS Times Cited 635] [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 94] [SCOPUS Times Cited 106] [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 301] [SCOPUS Times Cited 377] [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 83] [SCOPUS Times Cited 97] [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 136] [SCOPUS Times Cited 152] [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 21] [SCOPUS Times Cited 26] [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 426] [SCOPUS Times Cited 490] [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 75] [SCOPUS Times Cited 84] [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 123] [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 141] [SCOPUS Times Cited 159] [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 139] [SCOPUS Times Cited 150] [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 134] [SCOPUS Times Cited 149] [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 397] [SCOPUS Times Cited 452] [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 282] [SCOPUS Times Cited 345] [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. 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Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
