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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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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  3/2018 - 5

Graphical Interpretation of the Extended Kalman Filter: Estimating the State-of-Charge of a Lithium Iron Phosphate Cell

CIORTEA, F. See more information about CIORTEA, F. on SCOPUS See more information about CIORTEA, F. on IEEExplore See more information about CIORTEA, F. on Web of Science, NEMES, M. See more information about  NEMES, M. on SCOPUS See more information about  NEMES, M. on SCOPUS See more information about NEMES, M. on Web of Science, HINTEA, S. See more information about HINTEA, S. on SCOPUS See more information about HINTEA, S. on SCOPUS See more information about HINTEA, S. 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 (1,592 KB) | Citation | Downloads: 300 | Views: 716

Author keywords
battery management systems, electric vehicles, Kalman filters, Lithium batteries, parameter estimation

References keywords
kalman(11), battery(10), filter(8), extended(7), state(6), estimation(6), power(5), charge(5), optim(4), control(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 29 - 36
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03005
Web of Science Accession Number: 000442420900005
SCOPUS ID: 85052087917

Abstract
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Full text preview
Electric vehicles (EVs) fall in line with a new ideology of less waste and more conscious usage of resources, slowly picking up speed. In this context, energy storage is of paramount importance, making batteries a key element in the architecture of the electric vehicles. The state of the battery pack must be thoroughly monitored to prolong lifetime and extend vehicle range. For this, measurable physical quantities (i.e. terminal voltage, charge/discharge current, temperature) must be monitored and processed, while the inferred parameters (e.g. state-of-charge (SoC), state-of-health (SoH)) are computed and continuously updated. Whether we are talking about control of a noisy system, ill-defined decision-making processes or data analysis, estimation theory comes into play on a regular basis. The estimation algorithm is critical for appropriate usage of all available power, therefore, research effort is required to allow development of an optimum for a given application, by exploring design alternatives and their effects. This paper evaluates graphically an extended Kalman filter (EKF) for determining the SoC of lithium-ion batteries (LIBs) considering various cell models, initial conditions and charge/discharge profiles. The results are qualitatively and quantitatively assessed by extracting and visualizing the dynamics of the internal variables of the filter during operation.


References | Cited By  «-- Click to see who has cited this paper

[1] M. van der Steen, R. M. van Schelven, R. Kotter, M. J. W. van Twist and P. van Deventer, "EV policy compared: An international comparison of governments' policy strategy towards e-mobility," in E-Mobility in Europe, Springer International Publishing, Switzerland, 2015, pp. 27-53,
[CrossRef] [SCOPUS Times Cited 18]


[2] D. Doughty and E. P. Roth, "A general discussion of Li ion battery safety," The Electrochemical Society Interface, vol. 21, no. 2, pp. 37-44, Summer 2012,
[CrossRef] [SCOPUS Times Cited 227]


[3] D. A. Corrigan and A. Masias, "Batteries for electric and hybrid vehicles," in Linden's Handbook of Batteries, T. B. Reddy, 4th ed., New York: McGraw-Hill, 29.2 EV Battery Performance Targets, 2011.

[4] D. Belov and M. H. Yang, "Investigation of the kinetic mechanism in overcharge process for Li-ion battery," Solid State Ionics, vol. 179, no. 27-32, pp. 1816-1821, Sept. 2008,
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 72]


[5] H. Maleki and J. N. Howard, "Effects of overdischarge on performance and thermal stability of Li-ion cell," Journal Power Sources, vol. 160, no. 2, pp. 1395-1402, Oct. 2006,
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 94]


[6] H. Rahimi-Eichi, U. Ojha, F. Baronti and M.-Y. Chow, "Battery management system: an overview of its application in the smart grid and electric vehicles," IEEE Industrial Electronics Magazine, vol. 7, no. 2, pp. 4-16, June 2013,
[CrossRef] [Web of Science Times Cited 254] [SCOPUS Times Cited 301]


[7] K.-S. Ng, Y.-F. Huang, C.-S. Moo and Y.-C. Hsieh, "An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries," Intl. Telecommunications Energy Conf., Dec. 2009,
[CrossRef] [SCOPUS Times Cited 39]


[8] H. Dai, Z. Sun and X. Wei, "Online SOC estimation of high-power lithium-ion batteries used on HEVs," in ICVES, June 2007, pp. 342-347,
[CrossRef] [SCOPUS Times Cited 49]


[9] H. He, R. Xiong, X. Zhang, F. Sun and J. Fan, "State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model," IEEE Trans. Vehicular Technology, vol. 60, no. 4, pp. 1461-1469, May 2011,
[CrossRef] [Web of Science Times Cited 277] [SCOPUS Times Cited 350]


[10] Q. Yu, R. Xiong, C. Lin, W. Shen and J. Deng, "Lithium-ion battery parameters and state-of-charge joint estimation based on H infinity and unscented Kalman filters," IEEE Trans. Vehicular Technology,
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 48]


[11] Y. Wang, H. Fang, L. Zhou and T. Wada, "A methodical investigation of the extended Kalman filter approach," IEEE Control Systems Magazine, vol. 37, no. 4, pp. 73-96, July 2017,
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


[12] Y. Niu and L. Hu, "An extended Kalman filter application on moving object tracking," in Proc. 5th Intl Conf. Electrical Engineering and Automatic Control, Springer, Berlin, Heidelberg, 2016, pp. 1261-1268,
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[13] R. Faragher, "Understanding the basis of the Kalman filter via a simple and intuitive derivation," Signal Processing Magazine, vol. 29, no. 5, pp. 128-132, Sept. 2012,
[CrossRef] [Web of Science Times Cited 169] [SCOPUS Times Cited 217]


[14] T. Michalski, C. Lopez, A. Garcia and L. Romeral, "Sensorless control of five phase PMSM based on extended Kalman filter" Annual Conf. IEEE Industrial Electronics Society, Oct. 2016, pp. 2904-2909,
[CrossRef] [SCOPUS Times Cited 5]


[15] G. L. Plett, "Extended Kalman filter for battery management systems of LiPB-based HEV battery packs, Part1. Background," Journal Power Sources, vol. 134, no. 2, pp. 252-261, June 2004,
[CrossRef] [Web of Science Times Cited 539] [SCOPUS Times Cited 735]


[16] O. B. Belghith, L. Sbita and F. Bettaher, "Maximum power point tracking by the technique of the extended Kalman filter" in GECS, Oct. 2017,
[CrossRef] [SCOPUS Times Cited 1]


[17] A. A.-H. Hussein and I. Batarseh, "An overview of generic battery models," IEEE Power and Energy Society General Meeting, July 2011,
[CrossRef] [SCOPUS Times Cited 56]


[18] F. Ciortea, C. Rusu, M. Nemes and C. Gatea, "Extended Kalman filter for state-of-charge estimation in electric vehicle battery packs," in OPTIM, May 2017, pp. 611-616,
[CrossRef] [SCOPUS Times Cited 5]


[19] F. Ciortea, S. Hintea, C. Gatea and M. Nemes, "Measurement method and parametric modelling of LiFePO4 cell for SOC estimation in EVs," in OPTIM, May 2017, pp. 675-680,
[CrossRef] [SCOPUS Times Cited 2]


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References Weight

Web of Science® Citations for all references: 1,608 TCR
SCOPUS® Citations for all references: 3,297 TCR

Web of Science® Average Citations per reference: 73 ACR
SCOPUS® Average Citations per reference: 150 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 2019-08-23 08:39 in 142 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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