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Kalman filter for adaptive learning of look-up tables with application to automotive battery resistance estimation
RISE, Swedish ICT, Viktoria.
RISE, Swedish ICT, Viktoria.
2016 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 48, p. 78-86Article in journal (Refereed) Published
Abstract [en]

In online automotive applications, look-up tables are often used to model nonlinearities in component models that are to be valid over large operating ranges. If the component characteristics change with ageing or wear, these look-up tables must be updated online. Here, a method is presented where a Kalman filter is used to update the entire look-up table based on local estimation at the current operating conditions. The method is based on the idea that the parameter changes observed as a component ages are caused by physical phenomena having effect over a larger part of the operating range that may have been excited. This means that ageing patterns at different operating points are correlated, and these correlations are used to drive a random walk process that models the parameter changes. To demonstrate properties of the method, it is applied to estimate the ohmic resistance of a lithium-ion battery. In simulations the complete look-up table is successfully updated without problems of drift, even in parts of the operating range that are almost never excited. The method is also robust to uncertainties, both in the ageing model and in initial parameter estimates.

Place, publisher, year, edition, pages
2016. Vol. 48, p. 78-86
Keywords [en]
Automotive battery, Battery resistance estimation, Kalman filter, Li-ion battery, Look-up tables, Parameter estimation, Distance measurement, Electric batteries, Kalman filters, Lithium alloys, Lithium-ion batteries, Mobile devices, Ohmic contacts, Table lookup, Uncertainty analysis, Automotive applications, Component characteristics, Look up table, Operating condition, Physical phenomena, Random walk process, Resistance estimation
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-32615DOI: 10.1016/j.conengprac.2015.12.021Scopus ID: 2-s2.0-84954420249OAI: oai:DiVA.org:ri-32615DiVA, id: diva2:1156582
Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
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