Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Feb 2019
Next issue: May 2019
Avg review time: 81 days


PUBLISHER

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


TRAFFIC STATS

2,188,272 unique visits
571,964 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank


SEARCH ENGINES

aece.ro - Google Pagerank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 19 (2019)
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
 Volume 15 (2015)
 
     »   Issue 4 / 2015
 
     »   Issue 3 / 2015
 
     »   Issue 2 / 2015
 
     »   Issue 1 / 2015
 
 
  View all issues  








LATEST NEWS

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.

2017-Feb-16
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.

Read More »


    
 

  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: 193 | Views: 440

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
Quick view
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 15]


[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 196]


[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 65] [SCOPUS Times Cited 69]


[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 69] [SCOPUS Times Cited 83]


[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 221] [SCOPUS Times Cited 275]


[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,
[CrossRef] [Web of Science Times Cited 257] [SCOPUS Times Cited 319]


[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 27] [SCOPUS Times Cited 33]


[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 6] [SCOPUS Times Cited 11]


[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 1]


[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 131] [SCOPUS Times Cited 192]


[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 511] [SCOPUS Times Cited 698]


[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 53]


[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 1]


[20] R. M. Mehra, "On the identification of variances and adaptive Kalman filtering," IEEE Trans. Automatic Control, vol. AC-15, no. 2, pp. 175-184, Apr. 1970,
[CrossRef] [SCOPUS Times Cited 796]


[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,
[CrossRef] [Web of Science Times Cited 159] [SCOPUS Times Cited 220]




References Weight

Web of Science® Citations for all references: 1,447 TCR
SCOPUS® Citations for all references: 3,058 TCR

Web of Science® Average Citations per reference: 66 ACR
SCOPUS® Average Citations per reference: 139 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 2019-03-21 20:33 in 138 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2019
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.




Website loading speed and performance optimization powered by: