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State of Charge Estimation Accuracy in Charge Sustainable Mode of Hybrid Electric Vehicles
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Preparatory Inst Engn Studies Monastir, Tunisia.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.
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2017 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2017, Vol. 50, no 1, p. 2158-2163Conference paper, Published paper (Refereed)
Abstract [en]

The charge sustaining mode of a hybrid electric vehicle maintains the state of charge of the battery within a predetermined narrow band. Due to the poor system observability in this range, the state of charge estimation is tricky, and inadequate prior knowledge of the system uncertainties could lead to deterioration and divergence of estimates. In this paper, a comparative study of three estimators tuned based on the noise covariance matching technique is established in order to analyze their robustness in the state of charge estimation. Simulation results show a significant enhancement of filter accuracy using this adaptation. The adaptive particle filter has the best estimation results but it is vulnerable to model parameter uncertainties, further it is time consuming. On the other hand, the adaptive Unscented Kalman filter and the adaptive Extended Kalman filter show enough estimation accuracy, robustness for model uncertainty, and simplicity of implementation. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 50, no 1, p. 2158-2163
Series
IFAC Papersonline, E-ISSN 2405-8963
Keyword [en]
State of charge estimation; hybrid electric vehicle; adaptive Extended Kalman filter; adaptive Unscented Kalman filter; adaptive particle filter; noise covariance matrix tuning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-147218DOI: 10.1016/j.ifacol.2017.08.274ISI: 000423845200347OAI: oai:DiVA.org:liu-147218DiVA, id: diva2:1197309
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC)
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-12

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf