State of charge classification for lithium-ion batteries using impedance based features
Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV) to state of charge (SoC) characteristic of lithium ion batteries, m...
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2017-09-01
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Series: | Advances in Radio Science |
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doaj-0ca0d374f46946258c5f293d8ed561cb2020-11-24T21:14:34ZdeuCopernicus PublicationsAdvances in Radio Science 1684-99651684-99732017-09-0115939710.5194/ars-15-93-2017State of charge classification for lithium-ion batteries using impedance based featuresM. P. Felder0J. Götze1Information Processing Lab, TU Dortmund University, 44221 Dortmund, GermanyInformation Processing Lab, TU Dortmund University, 44221 Dortmund, GermanyCurrently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV) to state of charge (SoC) characteristic of lithium ion batteries, methods employed in applications with other cell chemistries cannot be adapted. Exploiting the higher significance of the impedance for state estimation for that chemistry, new impedance based features are proposed by this work. To evaluate the suitability of these features, simulations have been conducted using a simplified on-board power supply net as excitation source. The simulation outcome has been investigated regarding the cross correlation factor <i>r</i><sub><i>x</i><i>y</i></sub> and in a polynomial regression scenario. The results of the simulations show a best case error below 1 <i>%</i> SoC, which is 3 percentage points lower than using terminal voltage and impedance. When increasing the measurement uncertainty, the difference remains around 2 percent points.https://www.adv-radio-sci.net/15/93/2017/ars-15-93-2017.pdf |
collection |
DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
M. P. Felder J. Götze |
spellingShingle |
M. P. Felder J. Götze State of charge classification for lithium-ion batteries using impedance based features Advances in Radio Science |
author_facet |
M. P. Felder J. Götze |
author_sort |
M. P. Felder |
title |
State of charge classification for lithium-ion batteries using impedance based features |
title_short |
State of charge classification for lithium-ion batteries using impedance based features |
title_full |
State of charge classification for lithium-ion batteries using impedance based features |
title_fullStr |
State of charge classification for lithium-ion batteries using impedance based features |
title_full_unstemmed |
State of charge classification for lithium-ion batteries using impedance based features |
title_sort |
state of charge classification for lithium-ion batteries using impedance based features |
publisher |
Copernicus Publications |
series |
Advances in Radio Science |
issn |
1684-9965 1684-9973 |
publishDate |
2017-09-01 |
description |
Currently, the electrification of the drive train of passenger cars takes
place, and the task of obtaining precise knowledge about the condition of the
on board batteries gains importance. Due to a flat open circuit voltage (OCV)
to state of charge (SoC) characteristic of lithium ion batteries, methods
employed in applications with other cell chemistries cannot be adapted.
Exploiting the higher significance of the impedance for state estimation for
that chemistry, new impedance based features are proposed by this work. To
evaluate the suitability of these features, simulations have been conducted
using a simplified on-board power supply net as excitation source. The
simulation outcome has been investigated regarding the cross correlation
factor <i>r</i><sub><i>x</i><i>y</i></sub> and in a polynomial regression scenario. The results of the
simulations show a best case error below 1 <i>%</i> SoC, which is 3
percentage points lower than using terminal voltage and impedance. When
increasing the measurement uncertainty, the difference remains around 2
percent points. |
url |
https://www.adv-radio-sci.net/15/93/2017/ars-15-93-2017.pdf |
work_keys_str_mv |
AT mpfelder stateofchargeclassificationforlithiumionbatteriesusingimpedancebasedfeatures AT jgotze stateofchargeclassificationforlithiumionbatteriesusingimpedancebasedfeatures |
_version_ |
1716746671885385728 |