Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies
Battery state of charge (SOC) estimation is an important
parameter as it measures the total amount of electrical energy stored
at a current time. The SOC percentage acts as a fuel gauge if it
is compared with a conventional vehicle. Estimating the SOC is,
therefore, essential for monitoring the amount of useful life remaining
in the battery system. This paper looks at the implementation of three
nonlinear estimation strategies for Li-Ion battery SOC estimation.
One of the most common behavioral battery models is the one
state hysteresis (OSH) model. The extended Kalman filter (EKF),
the smooth variable structure filter (SVSF), and the time-varying
smoothing boundary layer SVSF are applied on this model, and the
results are compared.
[1] M. Farag, M. Fleckenstein, and S. Habibi, “Continuous piecewise-linear,
reduced-order electrochemical model for lithium-ion batteries in
real-time applications,” Journal of Power Sources, vol. 342, pp. 351–362,
feb 2017.
[2] B. Bhangu, P. Bentley, D. Stone, and C. Bingham, “Nonlinear observers
for predicting state-of-charge and state-of-health of lead-acid batteries
for hybrid-electric vehicles,” IEEE Trans. Veh. Technol., vol. 54, no. 3,
pp. 783–794, may 2005.
[3] A. Vasebi, S. Bathaee, and M. Partovibakhsh, “Predicting state of charge
of lead-acid batteries for hybrid electric vehicles by extended kalman
filter,” Energy Conversion and Management, vol. 49, no. 1, pp. 75–82,
jan 2008.
[4] T. Okoshi, K. Yamada, T. Hirasawa, and A. Emori, “Battery condition
monitoring (BCM) technologies about lead–acid batteries,” Journal of
Power Sources, vol. 158, no. 2, pp. 874–878, aug 2006.
[5] 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.
[6] “Extended kalman filtering for battery management systems of
lipb-based hev battery packs: Part 2. modeling and identification,”
Journal of power sources, vol. 134, no. 2, pp. 262–276, 2004.
[7] “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.
[8] M. Farag, S. Gadsden, S. Habibi, and J. Tjong, “A comparative study of
li-ion battery models and nonlinear dual estimation strategies,” in 2012
IEEE Transportation electrification conference and expo (ITEC). IEEE,
2012, pp. 1–8.
[9] M. Farag, M. Fleckenstein, and S. R. Habibi, “Li-ion battery SOC
estimation using non-linear estimation strategies based on equivalent
circuit models,” in SAE Technical Paper Series. SAE International, apr
2014.
[10] X. Hu, S. Li, and H. Peng, “A comparative study of equivalent circuit
models for li-ion batteries,” Journal of Power Sources, vol. 198, pp.
359–367, 2012.
[11] J. Kim, S. Lee, and B. H. Cho, “Complementary cooperation
algorithm based on DEKF combined with pattern recognition for
SOC/capacity estimation and SOH prediction,” IEEE Transactions on
Power Electronics, vol. 27, no. 1, pp. 436–451, jan 2012.
[12] B. D. O. Anderson, J. B. Moore, and M. Eslami, “Optimal filtering,”
IEEE Transactions on Systems, Man, and Cybernetics, vol. 12, no. 2,
pp. 235–236, 1982.
[13] W. L. Brogan, “Applied optimal estimation (arthur gels, ed.),” SIAM
Rev., vol. 19, no. 1, pp. 172–175, jan 1977.
[14] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice
with MATLAB. JOHN WILEY & SONS INC, 2014. (Online). Available:
http://www.ebook.de/de/product/23151381/mohinder s grewal angus
p andrews kalman filtering theory and practice with matlab.html
[15] S. Habibi, “The smooth variable structure filter,” Proceedings of the
IEEE, vol. 95, no. 5, pp. 1026–1059, may 2007.
[16] M. A. Al-Shabi, S. A. Gadsden, and S. R. Habibi, “The
toeplitz-observability smooth variable structure filter,” in 2013 IEEE
Jordan Conference on Applied Electrical Engineering and Computing
Technologies (AEECT). Institute of Electrical & Electronics Engineers
(IEEE), dec 2013.
[17] S. A. Gadsden and S. R. Habibi, “A new robust filtering strategy for
linear systems,” J. Dyn. Sys., Meas., Control, vol. 135, no. 1, p. 014503,
oct 2012.
[1] M. Farag, M. Fleckenstein, and S. Habibi, “Continuous piecewise-linear,
reduced-order electrochemical model for lithium-ion batteries in
real-time applications,” Journal of Power Sources, vol. 342, pp. 351–362,
feb 2017.
[2] B. Bhangu, P. Bentley, D. Stone, and C. Bingham, “Nonlinear observers
for predicting state-of-charge and state-of-health of lead-acid batteries
for hybrid-electric vehicles,” IEEE Trans. Veh. Technol., vol. 54, no. 3,
pp. 783–794, may 2005.
[3] A. Vasebi, S. Bathaee, and M. Partovibakhsh, “Predicting state of charge
of lead-acid batteries for hybrid electric vehicles by extended kalman
filter,” Energy Conversion and Management, vol. 49, no. 1, pp. 75–82,
jan 2008.
[4] T. Okoshi, K. Yamada, T. Hirasawa, and A. Emori, “Battery condition
monitoring (BCM) technologies about lead–acid batteries,” Journal of
Power Sources, vol. 158, no. 2, pp. 874–878, aug 2006.
[5] 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.
[6] “Extended kalman filtering for battery management systems of
lipb-based hev battery packs: Part 2. modeling and identification,”
Journal of power sources, vol. 134, no. 2, pp. 262–276, 2004.
[7] “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.
[8] M. Farag, S. Gadsden, S. Habibi, and J. Tjong, “A comparative study of
li-ion battery models and nonlinear dual estimation strategies,” in 2012
IEEE Transportation electrification conference and expo (ITEC). IEEE,
2012, pp. 1–8.
[9] M. Farag, M. Fleckenstein, and S. R. Habibi, “Li-ion battery SOC
estimation using non-linear estimation strategies based on equivalent
circuit models,” in SAE Technical Paper Series. SAE International, apr
2014.
[10] X. Hu, S. Li, and H. Peng, “A comparative study of equivalent circuit
models for li-ion batteries,” Journal of Power Sources, vol. 198, pp.
359–367, 2012.
[11] J. Kim, S. Lee, and B. H. Cho, “Complementary cooperation
algorithm based on DEKF combined with pattern recognition for
SOC/capacity estimation and SOH prediction,” IEEE Transactions on
Power Electronics, vol. 27, no. 1, pp. 436–451, jan 2012.
[12] B. D. O. Anderson, J. B. Moore, and M. Eslami, “Optimal filtering,”
IEEE Transactions on Systems, Man, and Cybernetics, vol. 12, no. 2,
pp. 235–236, 1982.
[13] W. L. Brogan, “Applied optimal estimation (arthur gels, ed.),” SIAM
Rev., vol. 19, no. 1, pp. 172–175, jan 1977.
[14] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice
with MATLAB. JOHN WILEY & SONS INC, 2014. (Online). Available:
http://www.ebook.de/de/product/23151381/mohinder s grewal angus
p andrews kalman filtering theory and practice with matlab.html
[15] S. Habibi, “The smooth variable structure filter,” Proceedings of the
IEEE, vol. 95, no. 5, pp. 1026–1059, may 2007.
[16] M. A. Al-Shabi, S. A. Gadsden, and S. R. Habibi, “The
toeplitz-observability smooth variable structure filter,” in 2013 IEEE
Jordan Conference on Applied Electrical Engineering and Computing
Technologies (AEECT). Institute of Electrical & Electronics Engineers
(IEEE), dec 2013.
[17] S. A. Gadsden and S. R. Habibi, “A new robust filtering strategy for
linear systems,” J. Dyn. Sys., Meas., Control, vol. 135, no. 1, p. 014503,
oct 2012.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:75075", author = "Mohammed Farag and Mina Attari and S. Andrew Gadsden and Saeid R. Habibi", title = "Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies", abstract = "Battery state of charge (SOC) estimation is an important
parameter as it measures the total amount of electrical energy stored
at a current time. The SOC percentage acts as a fuel gauge if it
is compared with a conventional vehicle. Estimating the SOC is,
therefore, essential for monitoring the amount of useful life remaining
in the battery system. This paper looks at the implementation of three
nonlinear estimation strategies for Li-Ion battery SOC estimation.
One of the most common behavioral battery models is the one
state hysteresis (OSH) model. The extended Kalman filter (EKF),
the smooth variable structure filter (SVSF), and the time-varying
smoothing boundary layer SVSF are applied on this model, and the
results are compared.", keywords = "State of charge estimation, battery modeling, one-state
hysteresis, filtering and estimation.", volume = "11", number = "3", pages = "237-5", }
