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
Access Full Article
topAbstract
topHow to cite
topAndreas 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
top- Aylor, J., Thieme, A. and Johnso, B. (1992). A battery state-of-charge indicator for electric wheelchairs, IEEE Transactions on Industrial Electronics 39(5): 398-409.
- Benger, R., Jiang, M., Beck, H., Wenzl, H., Ohms, D. and Schaedlich, G. (2009). Electrochemical and thermal modeling of lithium-ion cells for use in HEV or EV application, World Electric Vehicle Journal 3: 1-10, http://www.evs24.org/wevajournal/vol3/title.html.
- Benini, L., Castelli, G., Macii, A., Macii, E., Poncino, M. and Scarsi, R. (2001). Discrete-time battery models for system-level low-power design, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 9(5): 630-640.
- Bhangu, B., Bentley, P., Stone, D. and Bingham, C. (2005). Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles, IEEE Transactions on Vehicular Technology 54(3): 783-794.
- Bo, C., Zhifeng, B. and Binggang, C. (2008). State of charge estimation based on evolutionary neural network, Journal of Energy Conversion and Management 49(10): 2788-2794.
- Buller, S., Thele, M., Doncker, R. and Karden, E. (2005). Impedance based simulation models of supercapacitors and Li-ion batteries for power electronic applications, IEEE Transactions on Industry Applications 41(3): 742-747.
- Chan, C., Lo, E. and Weixiang, S. (2000). The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles, Journal of Power Sources 87(1-2): 201-204.
- Chen, M. and Rincon-Mora, G. (2006). Accurate electrical battery model capable of predicting runtime and I-V performance, IEEE Transactions on Energy Conversion 21(2): 504-511.
- Chiasson, J. and Vairamohan, B. (2005). Estimating the state of charge of a battery, IEEE Transactions on Control Systems Technology 13(3): 465-470.
- Erdinc, O., Vural, B. and Uzunoglu, M. (2009). A dynamic lithium-ion battery model considering the effects of temperature and capacity fading, International Conference on Clean Electrical Power, Capri, Italy, pp. 383-386.
- Gomadam, P., Weidner, J., Dougal, R. and White, R. (2002). Mathematical modeling of lithium-ion and nickel battery systems, Journal of Power Sources 110(2): 267-284.
- Gu, W. and Wang, C. (2000). Thermal-electrochemical modeling of battery systems, Journal of The Electrochemical Society 147(8): 2910-2922.
- Isidori, A. (1995). Nonlinear Control Systems, 1: An Introduction, Springer-Verlag, Berlin. Zbl0878.93001
- Johnson, V. (2002). Battery performance models in ADVISOR, Journal of Power Sources 110(2): 321-329.
- Junping, W., Jingang, G. and Lei, D. (2009). An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack, Journal of Energy Conversion and Management 50(12): 3182-3186.
- Kim, I. (2006). The novel state of charge estimation method for lithium battery using sliding mode observer, Journal of Power Sources 163(1): 584-590.
- Klein, R., Chaturvedi, N., Christensen, J., Ahmed, J., Findeisen, R. and Kojic, A. (2012). Electrochemical model based observer design for a lithium-ion battery, IEEE Transactions on Control Systems Technology PP(99): 1-13.
- Krener, J. and Isidori, A. (1983). Linearization by output injection and nonlinear observers, Journal of Systems and Control Letters 3(1): 47-52. Zbl0524.93030
- Leska, M., Prabel, R., Rauh, A. and Aschemann, H. (2011). Simulation and optimization of the longitudinal dynamics of parallel hybrid railway vehicles, in E. Schnieder and G. Tarnai (Eds.), FORMS/FORMAT 2010, Springer, Berlin/Heidelberg, pp. 155-164.
- Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control, International Journal of Control 76(9-10): 924-941. Zbl1049.93014
- Mohinder, S. and Andrews, P. (2001). Kalman Filtering: Theory and Practice-Using MATLAB, 2nd Edn., Wiley-Interscience, Hoboken, NJ. Zbl1322.93001
- Pang, S., Farrell, J., Du, J. and Barth, M. (2001). Battery state-of-charge estimation, American Control Conference, Arlington, VA, USA, Vol. 2, pp. 1644-1649.
- Plett, G. (2004a). Extended Kalman filtering for battery management systems of LIPB-based HEV battery packs, Part 1: Background, Journal of Power Sources 134(2): 252-261.
- Plett, G. (2004b). Extended Kalman filtering for battery management systems of LIPB-based HEV battery packs, Part 2: Modeling and identification, Journal of Power Sources 134(2): 262-276.
- Plett, G. (2004c). Extended Kalman filtering for battery management systems of LIPB-based HEV battery packs, Part 3: State and parameter estimation, Journal of Power Sources 134(2): 277-292.
- Rauh, A. and Aschemann, H. (2012). Sensitivity-based state and parameter estimation for lithium-ion battery systems, 9th International Conference on System Identification and Control Problems, SICPRO'12, Moscow, Russia, pp. 469-485.
- Rauh, A., Minisini, J. and Hofer, E. (2009). Verification techniques for sensitivity analysis and design of controllers for nonlinear dynamic systems with uncertainties, International Journal of Applied Mathematics and Computer Science 19(3): 425-439, DOI: 10.2478/v10006-009-0035-1. Zbl1300.93060
- Rauh, A., Weitschat, R. and Aschemann, H. (2010). Modellgestützter Beobachterentwurf zur Betriebszustandsund Alterungserkennung für Lithium-Ionen-Batterien, VDI-Berichte 2105: Innovative Fahrzeugantriebe 2010 Die Vielfalt der Mobilitt: Vom Verbrenner bis zum E-Motor: 7. VDI-Tagung Innovative Fahrzeugantriebe, Dresden, Germany, pp. 377-382.
- Remmlinger, J., Buchholz, M., Meiler, M., Bernreuter, P. and Dietmayer, K. (2011). State-of-health monitoring of lithium-ion batteries in electric vehicles by on board internal resistance estimation, Journal of Power Sources 196(12): 5357-5363.
- Rong, P. and Pedram, M. (2006). An analytical model for predicting the remaining battery capacity of lithium-ion batteries, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 14(5): 441-451.
- Salameh, Z., Casacca, M. and Lynch, W. (1992). A mathematical model for lead-acid batteries, IEEE Transactions on Energy Conversion 7(1): 93-98.
- Serrao, L., Chehab, Z., Guezennee, Y. and Rizzoni, G. (2005). An aging model of Ni-MH batteries for hybrid electric vehicles, IEEE Conference on Vehicle Power and Propulsion, Chicago, IL, USA, pp. 78-85.
- Shen, Y. (2010). Adaptive online state-of-charge determination based on neuro-controller and neural network, Journal of Energy Conversion and Management 51(5): 1093-1098.
- Smith, K., Rahn, C. and Wang, C. (2010). Model-based electrochemical estimation and constraint management for pulse operation of lithium ion batteries, IEEE Transactions on Control Systems Technology 3(18): 654-663.
- Stengel, R. (1994). Optimal Control and Estimation, Dover Publications, Inc, Mineola, NY. Zbl0854.93001
- Wang, C. and Srinivasan, V. (2002). Computational battery dynamics (CBD)-electrochemical/thermal coupled modeling and multi-scale modeling, Journal of Power Sources 110(2): 364-376.
- Xu, D., Jiang, B., Shi, P. (2012). Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model, International Journal of Applied Mathematics and Computer Science 22(1): 183-196, DOI: 10.2478/v10006-012-0014-9. Zbl1273.93105
- Zeitz, M. (1987). The extended Luenberger observer for nonlinear systems, Systems and Control Letters 9(2): 149-156. Zbl0624.93012
- Zhang, F., Liu, G. and Fang, L. (2008). A battery state of charge estimation method using sliding mode observer, 7th World Congress on Intelligent Control and Automation, WCICA 2008, Chongqing, China, pp. 989-994.
- Zhirabok, A. and Shumsky, A. (2012). An approach to the analysis of observability and controllability in nonlinear systems via linear methods, International Journal of Applied Mathematics and Computer Science 22(3): 507-522, DOI: 10.2478/v10006-012-0038-1. Zbl1302.93048
Citations in EuDML Documents
top- Andreas Rauh, Luise Senkel, Harald Aschemann, Vasily V. Saurin, Georgy V. Kostin, An integrodifferential approach to modeling, control, state estimation and optimization for heat transfer systems
- Benoît Schwaller, Denis Ensminger, Birgitta Dresp-Langley, José Ragot, State estimation for miso non-linear systems in controller canonical form
NotesEmbed ?
topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.