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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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A Phasor Estimation Algorithm based on Hilbert Transform for P-class PMUs, RAZO-HERNANDEZ, J. R., VALTIERRA-RODRIGUEZ, M., GRANADOS-LIEBERMAN, D., TAPIA-TINOCO, G., RODRIGUEZ-RODRIGUEZ, J. R.
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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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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
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Download PDF pdficon (1,592 KB) | Citation | Downloads: 272 | Views: 608

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

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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

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[CrossRef] [SCOPUS Times Cited 16]

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[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,
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[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 37]

[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 48]

[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,
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[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,
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[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 7] [SCOPUS Times Cited 12]

[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 152] [SCOPUS Times Cited 211]

[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]

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[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,
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[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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[21] W. Ding, J. Wang and C. Rizos, "Improving adaptive Kalman estimation in GPS/INS integration," The Journal of Navigation, vol. 6, no. 3, pp. 517-529, Aug. 2017,
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References Weight

Web of Science® Citations for all references: 1,542 TCR
SCOPUS® Citations for all references: 3,197 TCR

Web of Science® Average Citations per reference: 70 ACR
SCOPUS® Average Citations per reference: 145 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 2019-06-16 16:01 in 141 seconds.

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

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