Nonlinear state observers and extended Kalman filters for battery systems

Andreas Rauh; Saif S. Butt; Harald Aschemann

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 3, page 539-556
  • ISSN: 1641-876X

Abstract

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The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations are directly accessible by measurements. Moreover, this work provides a comparison of the performance of different observer and filtering techniques as well as a development of estimation procedures that guarantee a reliable detection of large parameter variations. For that reason, different charging and discharging current profiles of batteries are investigated by numerical simulations. The estimation procedures considered in this paper are, firstly, a nonlinear Luenberger-type state observer with an offline calculated gain scheduling approach, secondly, a continuous-time extended Kalman filter and, thirdly, a hybrid extended Kalman filter, where the corresponding filter gains are computed online.

How to cite

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Andreas Rauh, Saif S. Butt, and Harald Aschemann. "Nonlinear state observers and extended Kalman filters for battery systems." International Journal of Applied Mathematics and Computer Science 23.3 (2013): 539-556. <http://eudml.org/doc/262303>.

@article{AndreasRauh2013,
abstract = {The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations are directly accessible by measurements. Moreover, this work provides a comparison of the performance of different observer and filtering techniques as well as a development of estimation procedures that guarantee a reliable detection of large parameter variations. For that reason, different charging and discharging current profiles of batteries are investigated by numerical simulations. The estimation procedures considered in this paper are, firstly, a nonlinear Luenberger-type state observer with an offline calculated gain scheduling approach, secondly, a continuous-time extended Kalman filter and, thirdly, a hybrid extended Kalman filter, where the corresponding filter gains are computed online.},
author = {Andreas Rauh, Saif S. Butt, Harald Aschemann},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {observers; state estimation; Riccati equations; extended Kalman filters; parameter estimation},
language = {eng},
number = {3},
pages = {539-556},
title = {Nonlinear state observers and extended Kalman filters for battery systems},
url = {http://eudml.org/doc/262303},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Andreas Rauh
AU - Saif S. Butt
AU - Harald Aschemann
TI - Nonlinear state observers and extended Kalman filters for battery systems
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 3
SP - 539
EP - 556
AB - The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations are directly accessible by measurements. Moreover, this work provides a comparison of the performance of different observer and filtering techniques as well as a development of estimation procedures that guarantee a reliable detection of large parameter variations. For that reason, different charging and discharging current profiles of batteries are investigated by numerical simulations. The estimation procedures considered in this paper are, firstly, a nonlinear Luenberger-type state observer with an offline calculated gain scheduling approach, secondly, a continuous-time extended Kalman filter and, thirdly, a hybrid extended Kalman filter, where the corresponding filter gains are computed online.
LA - eng
KW - observers; state estimation; Riccati equations; extended Kalman filters; parameter estimation
UR - http://eudml.org/doc/262303
ER -

References

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