Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method
Equivalent circuit models (ECMs) are widely used in
battery management systems in electric vehicles and other battery
energy storage systems. The battery dynamics and the model
parameters vary under different working conditions, such as different
temperature and state of charge (SOC) levels, and therefore online
parameter identification can improve the modelling accuracy. This
paper presents a way of online ECM parameter identification using a
continuous time (CT) estimation method. The CT estimation method
has several advantages over discrete time (DT) estimation methods
for ECM parameter identification due to the widely separated battery
dynamic modes and fast sampling. The presented method can be used
for online SOC estimation. Test data are collected using a lithium ion
cell, and the experimental results show that the presented CT method
achieves better modelling accuracy compared with the conventional
DT recursive least square method. The effectiveness of the presented
method for online SOC estimation is also verified on test data.
[1] M. Chen and G. A. Rincon-Mora, “Accurate electrical battery model
capable of predicting runtime and IV performance,” Energy conversion,
IEEE Transactions on, vol. 21, no. 2, pp. 504–511, 2006.
[2] G. L. Plett, “Extended kalman filtering for battery management systems
of lipb-based HEV battery packs: Part 1. background,” Journal of Power
sources, vol. 134, no. 2, pp. 252–261, 2004.
[3] C. Zhang, K. Li, L. Pei, and C. Zhu, “An integrated approach
for real-time model-based state-of-charge estimation of lithium-ion
batteries,” Journal of Power Sources, vol. 283, pp. 24–36, 2015.
[4] C. Zhang, K. Li, J. Deng, and S. Song, “Improved realtime
state-of-charge estimation of lifepo4 battery based on a novel
thermoelectric model,” Industrial Electronics, IEEE Transactions on, p.
accepted, 2016.
[5] W. Widanage, A. Barai, G. Chouchelamane, K. Uddin, A. McGordon,
J. Marco, and P. Jennings, “Design and use of multisine signals for li-ion
battery equivalent circuit modelling. part 2: Model estimation,” Journal
of Power Sources, vol. 324, pp. 61–69, 2016.
[6] M. Verbrugge and E. Tate, “Adaptive state of charge algorithm for
nickel metal hydride batteries including hysteresis phenomena,” Journal
of Power Sources, vol. 126, no. 1, pp. 236–249, 2004.
[7] M. Verbrugge and B. Koch, “Generalized recursive algorithm for
adaptive multiparameter regression application to lead acid, nickel metal
hydride, and lithium-ion batteries,” Journal of The Electrochemical
Society, vol. 153, no. 1, pp. A187–A201, 2006.
[8] H. Rahimi-Eichi, F. Baronti, and M.-Y. Chow, “Online adaptive
parameter identification and state-of-charge coestimation for
lithium-polymer battery cells,” IEEE Transactions on Industrial
Electronics, vol. 61, no. 4, pp. 2053–2061, 2014.
[9] R. Xiong, F. Sun, X. Gong, and C. Gao, “A data-driven based adaptive
state of charge estimator of lithium-ion polymer battery used in electric
vehicles,” Applied Energy, vol. 113, pp. 1421–1433, 2014.
[10] H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo, “Online model-based
estimation of state-of-charge and open-circuit voltage of lithium-ion
batteries in electric vehicles,” Energy, vol. 39, no. 1, pp. 310–318, 2012. [11] V.-H. Duong, H. A. Bastawrous, K. Lim, K. W. See, P. Zhang, and
S. X. Dou, “Online state of charge and model parameters estimation of
the lifepo4 battery in electric vehicles using multiple adaptive forgetting
factors recursive least-squares,” Journal of Power Sources, vol. 296, pp.
215–224, 2015.
[12] Z. Wei, K. J. Tseng, N. Wai, T. M. Lim, and M. Skyllas-Kazacos,
“Adaptive estimation of state of charge and capacity with online
identified battery model for vanadium redox flow battery,” Journal of
Power Sources, vol. 332, pp. 389–398, 2016.
[13] G. L. Plett, “Extended kalman filtering for battery management systems
of lipb-based HEV battery packs: Part 3. state and parameter estimation,”
Journal of Power sources, vol. 134, no. 2, pp. 277–292, 2004.
[14] ——, “Sigma-point kalman filtering for battery management systems of
lipb-based HEV battery packs: Part 2: Simultaneous state and parameter
estimation,” Journal of Power Sources, vol. 161, no. 2, pp. 1369–1384,
2006.
[15] Y. Hu and Y.-Y. Wang, “Two time-scaled battery model identification
with application to battery state estimation,” IEEE Transactions on
Control Systems Technology, vol. 23, no. 3, pp. 1180–1188, 2015.
[16] H. Garnier, L. Wang, and P. C. Young, “Direct identification of
continuous-time models from sampled data: Issues, basic solutions and
relevance,” in Identification of continuous-time models from sampled
data. Springer, 2008, pp. 1–29.
[17] H. Garnier, M. Mensler, and A. Richard, “Continuous-time model
identification from sampled data: implementation issues and
performance evaluation,” International Journal of Control, vol. 76,
no. 13, pp. 1337–1357, 2003.
[18] H. Unbehauen and G. Rao, “A review of identification in
continuous-time systems,” Annual reviews in Control, vol. 22, pp.
145–171, 1998.
[19] N. Rao Sripada and D. Grant Fisher, “Improved least squares
identification,” International Journal of Control, vol. 46, no. 6, pp.
1889–1913, 1987.
[20] A. Barai, W. D. Widanage, J. Marco, A. McGordon, and P. Jennings,
“A study of the open circuit voltage characterization technique and
hysteresis assessment of lithium-ion cells,” Journal of Power Sources,
vol. 295, pp. 99–107, 2015.
[21] A. Barr´e, B. Deguilhem, S. Grolleau, M. G´erard, F. Suard, and D. Riu,
“A review on lithium-ion battery ageing mechanisms and estimations
for automotive applications,” Journal of Power Sources, vol. 241, pp.
680–689, 2013.
[22] S. Sagara and Z.-Y. Zhao, “Numerical integration approach to on-line
identification of continuous-time systems,” Automatica, vol. 26, no. 1,
pp. 63–74, 1990.
[23] L. Dugard and I. Landau, “Recursive output error identification
algorithms theory and evaluation,” Automatica, vol. 16, no. 5, pp.
443–462, 1980.
[1] M. Chen and G. A. Rincon-Mora, “Accurate electrical battery model
capable of predicting runtime and IV performance,” Energy conversion,
IEEE Transactions on, vol. 21, no. 2, pp. 504–511, 2006.
[2] G. L. Plett, “Extended kalman filtering for battery management systems
of lipb-based HEV battery packs: Part 1. background,” Journal of Power
sources, vol. 134, no. 2, pp. 252–261, 2004.
[3] C. Zhang, K. Li, L. Pei, and C. Zhu, “An integrated approach
for real-time model-based state-of-charge estimation of lithium-ion
batteries,” Journal of Power Sources, vol. 283, pp. 24–36, 2015.
[4] C. Zhang, K. Li, J. Deng, and S. Song, “Improved realtime
state-of-charge estimation of lifepo4 battery based on a novel
thermoelectric model,” Industrial Electronics, IEEE Transactions on, p.
accepted, 2016.
[5] W. Widanage, A. Barai, G. Chouchelamane, K. Uddin, A. McGordon,
J. Marco, and P. Jennings, “Design and use of multisine signals for li-ion
battery equivalent circuit modelling. part 2: Model estimation,” Journal
of Power Sources, vol. 324, pp. 61–69, 2016.
[6] M. Verbrugge and E. Tate, “Adaptive state of charge algorithm for
nickel metal hydride batteries including hysteresis phenomena,” Journal
of Power Sources, vol. 126, no. 1, pp. 236–249, 2004.
[7] M. Verbrugge and B. Koch, “Generalized recursive algorithm for
adaptive multiparameter regression application to lead acid, nickel metal
hydride, and lithium-ion batteries,” Journal of The Electrochemical
Society, vol. 153, no. 1, pp. A187–A201, 2006.
[8] H. Rahimi-Eichi, F. Baronti, and M.-Y. Chow, “Online adaptive
parameter identification and state-of-charge coestimation for
lithium-polymer battery cells,” IEEE Transactions on Industrial
Electronics, vol. 61, no. 4, pp. 2053–2061, 2014.
[9] R. Xiong, F. Sun, X. Gong, and C. Gao, “A data-driven based adaptive
state of charge estimator of lithium-ion polymer battery used in electric
vehicles,” Applied Energy, vol. 113, pp. 1421–1433, 2014.
[10] H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo, “Online model-based
estimation of state-of-charge and open-circuit voltage of lithium-ion
batteries in electric vehicles,” Energy, vol. 39, no. 1, pp. 310–318, 2012. [11] V.-H. Duong, H. A. Bastawrous, K. Lim, K. W. See, P. Zhang, and
S. X. Dou, “Online state of charge and model parameters estimation of
the lifepo4 battery in electric vehicles using multiple adaptive forgetting
factors recursive least-squares,” Journal of Power Sources, vol. 296, pp.
215–224, 2015.
[12] Z. Wei, K. J. Tseng, N. Wai, T. M. Lim, and M. Skyllas-Kazacos,
“Adaptive estimation of state of charge and capacity with online
identified battery model for vanadium redox flow battery,” Journal of
Power Sources, vol. 332, pp. 389–398, 2016.
[13] G. L. Plett, “Extended kalman filtering for battery management systems
of lipb-based HEV battery packs: Part 3. state and parameter estimation,”
Journal of Power sources, vol. 134, no. 2, pp. 277–292, 2004.
[14] ——, “Sigma-point kalman filtering for battery management systems of
lipb-based HEV battery packs: Part 2: Simultaneous state and parameter
estimation,” Journal of Power Sources, vol. 161, no. 2, pp. 1369–1384,
2006.
[15] Y. Hu and Y.-Y. Wang, “Two time-scaled battery model identification
with application to battery state estimation,” IEEE Transactions on
Control Systems Technology, vol. 23, no. 3, pp. 1180–1188, 2015.
[16] H. Garnier, L. Wang, and P. C. Young, “Direct identification of
continuous-time models from sampled data: Issues, basic solutions and
relevance,” in Identification of continuous-time models from sampled
data. Springer, 2008, pp. 1–29.
[17] H. Garnier, M. Mensler, and A. Richard, “Continuous-time model
identification from sampled data: implementation issues and
performance evaluation,” International Journal of Control, vol. 76,
no. 13, pp. 1337–1357, 2003.
[18] H. Unbehauen and G. Rao, “A review of identification in
continuous-time systems,” Annual reviews in Control, vol. 22, pp.
145–171, 1998.
[19] N. Rao Sripada and D. Grant Fisher, “Improved least squares
identification,” International Journal of Control, vol. 46, no. 6, pp.
1889–1913, 1987.
[20] A. Barai, W. D. Widanage, J. Marco, A. McGordon, and P. Jennings,
“A study of the open circuit voltage characterization technique and
hysteresis assessment of lithium-ion cells,” Journal of Power Sources,
vol. 295, pp. 99–107, 2015.
[21] A. Barr´e, B. Deguilhem, S. Grolleau, M. G´erard, F. Suard, and D. Riu,
“A review on lithium-ion battery ageing mechanisms and estimations
for automotive applications,” Journal of Power Sources, vol. 241, pp.
680–689, 2013.
[22] S. Sagara and Z.-Y. Zhao, “Numerical integration approach to on-line
identification of continuous-time systems,” Automatica, vol. 26, no. 1,
pp. 63–74, 1990.
[23] L. Dugard and I. Landau, “Recursive output error identification
algorithms theory and evaluation,” Automatica, vol. 16, no. 5, pp.
443–462, 1980.
@article{"International Journal of Electrical, Electronic and Communication Sciences:75095", author = "Cheng Zhang and James Marco and Walid Allafi and Truong Q. Dinh and W. D. Widanage", title = "Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method", abstract = "Equivalent circuit models (ECMs) are widely used in
battery management systems in electric vehicles and other battery
energy storage systems. The battery dynamics and the model
parameters vary under different working conditions, such as different
temperature and state of charge (SOC) levels, and therefore online
parameter identification can improve the modelling accuracy. This
paper presents a way of online ECM parameter identification using a
continuous time (CT) estimation method. The CT estimation method
has several advantages over discrete time (DT) estimation methods
for ECM parameter identification due to the widely separated battery
dynamic modes and fast sampling. The presented method can be used
for online SOC estimation. Test data are collected using a lithium ion
cell, and the experimental results show that the presented CT method
achieves better modelling accuracy compared with the conventional
DT recursive least square method. The effectiveness of the presented
method for online SOC estimation is also verified on test data.", keywords = "Equivalent circuit model, continuous time domain
estimation, linear integral filter method, parameter and SOC
estimation, recursive least square.", volume = "11", number = "3", pages = "277-6", }
