Comparison Between Recursive Least Squares Method and Kalman Filter for Online Identification of Supercapacitor State of Health

  • Ms. Zoubida Bououchma ENSAM-Meknès
  • Pr. Jalal Sabor Department of Control, Piloting and Supervision of systems (CP2S), ENSAM-Engineering School, Moulay Ismail University, Meknes, Morocco
Keywords: Supercapacitor, Identification, Capacitance, Resistance, Recursive least squares (RLS), Kalman Filter (KF).

Abstract

Supercapacitors are electrochemical components with high-power density and an intermediate energy density between batteries and conventional capacitors. They are characterized by low series resistance, signifificant equivalent capacitance, and long service life. Nowadays, they have become an attractive alternative storage device for several applications. However, supercapacitors are subject to degradation due to aging, in addition to other factors, such as temperature and high voltage. Therefore, it is very important to be able to estimate their State-of-Health during operation. Electrochemical Impedance Spectroscopy and Maxwell test are very recognized techniques to determine supercapacitors’ state-of-health. However, these methods require the interruption of system operation and thus cannot be performed in real-time (online). The purpose of this paper is the real-time estimation of supercapacitor resistance and capacitance, which are the main indicators of supercapacitor state-of-health. The electrical behavior of the supercapacitor is modeled using equivalent RC circuit model and the identifification is performed using two methods: recursive least squares method and Kalman fifilter. The resistance and the capacitance values obtained with the two methods are compared with capacitance and resistance values using Maxwell experimental test. The values obtained by Kalman fifilter are more accurate for both resistance and capacitance.  

Author Biographies

Ms. Zoubida Bououchma, ENSAM-Meknès
Zoubida Bououchma was born in Morocco in 1992. She received the Engineer degree in Electromechanical Engineering in 2015 from the Ecole Nationale Supérieure d’Arts & Métiers of Meknes (ENSAM-Meknes), Morocco. She is currently a Ph.D. student in electrical engineering in ENSAM-Meknes. His research interests are in the area of energy storage, more specifically, supercapacitors and Lithium-ion batteries, State-of-Health and State-of-Charge diagnosis and aging estimation.
Pr. Jalal Sabor, Department of Control, Piloting and Supervision of systems (CP2S), ENSAM-Engineering School, Moulay Ismail University, Meknes, Morocco
Jalal Sabor received the PhD degree in engineering science from the Institut National des Sciences Appliquées (INSA), Rouen, France in 1995. He is currently a full Professor of industrial computer science at the Ecole Nationale Supérieure d’Arts & Métiers (ENSAM) Moulay Ismail university, Meknes, Morocco. He is a member of the LSMI Laboratory, He is also the research team control steering and supervision systems head. His actual main research interests concern intelligent management of energy, smart grid, control and supervision systems, Architecture Based on Multi Agents Systems and fuzzy logic.

References

J. R. Miller and P. Simon, MATERIALS SCIENCE: Electrochemical Capacitors for Energy Management, Science, vol. 321, no 5889, p. 651-652, aoˆut 2008.

A. Burke, Ultracapacitors: why, how, and where is the technology, Journal of Power Sources, vol. 91, no 1, p. 37-50, nov. 2000.

Poonam, K. Sharma, A. Arora and S. K. Tripathi, Review of supercapacitors: Materials and devices, Journal of Energy Storage, vol. 21, p. 801-825, f´evr. 2019.

B. Yang, and al, Applications of battery/supercapacitor hybrid energy storage systems for electric vehicles using perturbation observer based robust control, Journal of Power Sources, vol. 448, p. 227444, f´evr. 2020.

A. Muzaffar, M. B. Ahamed, K. Deshmukh, and J. Thirumalai, A review on recent advances in hybrid supercapacitors: Design, fabrication and applications, Renewable and Sustainable Energy Reviews, vol. 101, p. 123-145, mars 2019.

A. El Mejdoubi, H. Chaoui, H. Gualous, and J. Sabor, Online Parameter Identification for Supercapacitor State-of-Health Diagnosis for Vehicular Applications, IEEE Trans. Power Electron., vol. 32, no 12, p. 9355-9363, d´ec. 2017.

J. Solano Martinez, D. Hissel, M.-C. Pera,and M. Amiet Practical Control Structure and Energy Management of a Testbed Hybrid Electric Vehicle, IEEE Trans. Veh. Technol., vol. 60, no 9, p. 4139-4152, nov. 2011.

Q. Zhang, and G. Li, Experimental Study on a Semi-Active Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicle Application, IEEE Trans. Power Electron., vol. 35, no 1, p. 1014-1021, janv. 2020.

R. K¨otz, and M. Carlen, Principles and applications of electrochemical capacitors, Electrochimica Acta, vol. 45, no 15-16, p. 2483-2498, mai 2000.

R. K¨otz, P. W. Ruch, and D. Cericola, Aging and failure mode of electrochemical double layer capacitors during accelerated constant load tests, Journal of Power Sources, vol. 195, no 3, p. 923-928, f´evr. 2010.

H. Gualous, R. Gallay, M. Al Sakka, A. Oukaour, B. Tala-Ighil, and B. Boudart, Calendar and cycling ageing of activated carbon supercapacitor for automotive application, Microelectronics Reliability, vol. 52, no 9-10, p. 2477-2481, sept. 2012.

S. Buller, E. Karden, D. Kok,and R. W. De Doncker, Modeling the dynamic behavior of supercapacitors using impedance spectroscopy, IEEE Trans. on Ind. Applicat., vol. 38, no 6, p. 1622-1626, nov. 2002.

A. Eddahech, O. Briat, N. Bertrand, J.-Y. Del´etage, and J.-M. Vinassa, Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks, International Journal of Electrical Power and Energy Systems, vol. 42, no 1, p. 487-494, nov. 2012.

Maxwell technology, Test Procedures for Capacitance, ESR, Leakage Current and Self-Discharge Char-acterizations of

Ultracapacitors, Maxwell Technologies Application Note, juin 2015. .

Z. Bououchma, J. Sabor, and H. Aitbouh,, New electrical model of supercapacitors for electric hybrid vehicle applications, Materials Today: Proceedings, vol. 13, p. 688-697, 2019.

Zubieta, and R. Bonert, Characterization of double-layer capacitors for power electronics applications, IEEE Trans. on Ind. Applicat., vol. 36, no 1, p. 199-205, f´evr. 2000.

R. German, P. Venet, A. Sari, O. Briat, and J. M. Vinassa, Comparison of EDLC impedance models used for ageing monitoring First International Conference on Renewable Energies and Vehicular Technology, Hammamet, mars 2012, p. 224-229..

L. Zhang, Z. Wang, X. Hu, F. Sun, and D. G. Dorrell, A comparative study of equivalent circuit models of ultracapacitors for electric vehicles, in Proc. Journal of Power Sources, vol. 274, p. 899-906, janv. 2015.

R. German, A. Hammar, R. Lallemand, A. Sari, and P. Venet, Novel Experimental Identification Method for a Supercapacitor Multipore Model in Order to Monitor the State of Health, IEEE Trans. Power Electron., vol. 31, no 1, p. 548-559, janv. 2016.

A. Nadeau, G. Sharma, and T. Soyata, State-of-charge estimation for supercapacitors: A Kalman filtering formulation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, mai 2014, p. 2194-2198..

L. Sanchez, S. Infante, J. Marcano, and V. Griffin, Polynomial Chaos based on the parallelized ensemble Kalman filter to estimate precipitation states, Stat., optim. inf. comput., vol. 3, no 1, p. 79-95, f´evr. 2015.

A. Mejdoubi, H. Chaoui, Hamid. Gualous, A. Oukaour, Y. Slamani, and J. Sabor, Supercapacitors State-of-Health Diagnosis for Electric Vehicle Applications, WEVJ, vol. 8, no 2, p. 379-387, juin 2016.

P. Saha and M. Khanra,, Online Estimation of State-of-Charge, State-of-Health and Temperature of Supercapacitor, IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, oct. 2020, p.1-5.

F. Naseri, E. Farjah, T. Ghanbari, Z. Kazemi, E. Schaltz, and J.-L. Schanen, Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications , IEEE Trans. Ind. Electron., vol. 67, no 9, p. 7963-7972, sept. 2020.

A. Soualhi and al., Heath Monitoring of Capacitors and Supercapacitors Using the Neo-Fuzzy Neural Approach, IEEE Trans. Ind. Inf., vol. 14, no 1, p. 24-34, janv. 2018.

D. Roman, S. Saxena, J. Bruns, R. Valentin, M. Pecht, and D. Flynn, A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature, IEEE Access, vol. 9, p. 25544-25553, 2021.

N. Rezazadeh,, Applying Bayesian Decision Theory in RBF Neural Network to Improve Network precision in Data Classification , Stat., optim. inf. comput., vol. 6, no 4, p. 588-599, nov. 2018.

K. Laadjal and A. J. Marques Cardoso, A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges, Int J Energy Res, p. er.7121, juill. 2021.

A. Eddahech, O. Briat, M. Ayadi, and J.-M. Vinassa, Modeling and adaptive control for supercapacitor in automotive applications based on artificial neural networks, Electric Power Systems Research, vol. 106, p. 134-141, janv. 2014.

H. Miniguano, A. Barrado, C. Fern´andez, P. Zumel, and A. L´azaro, A General Parameter Identification Procedure Used for the Comparative Study of Supercapacitors Models, S Energies, vol. 12, no 9, p. 1776, mai 2019.

Maxwell technology, Boostcap Ultracapacitor Module Operating Manual , janv. 2003. [in line]. available on: www.Maxwell.com.

L. Zhang, X. Hu, Z. Wang, F. Sun, and D. G. Dorrell, A review of supercapacitor modeling, estimation, and applications: A control/management perspective, Renewable and Sustainable Energy Reviews, vol. 81, p. 1868-1878, janv. 2018.

M. H. Hayes, Statistical digital signal processing and modeling, New York: John Wiley and Sons, 1996.

M. Nikkhoo, E. Farjah, and T. Ghanbari, A simple method for parameters identification of three branches model of supercapacitors, 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, Iran, mai 2016, p. 1586-1590.

Q. Li, R. Li, K. Ji, and W. Dai, Kalman Filter and Its Application, 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China, nov. 2015, p. 74-77.

Z. Chen, Y. Fu, and C. C. Mi, State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering, IEEE Trans. Veh. Technol., vol. 62, no 3, p. 1020-1030, mars 2013.

E. R. Kozhanova and A. A. Zaharov, Application of wavelet analysis to determine the parameters of the normal distribution law, International Conference on Actual Problems of Electron Devices Engineering (APEDE), Saratov, Russia, sept. 2014, p. 280-283.

B. Gou, Jacobian Matrix-Based Observability Analysis for State Estimation, IEEE Trans. Power Syst., vol. 21, no 1, Art. no 1, f´evr. 2006.

Z. Shi, F. Auger, E. Schaeffer, Ph. Guillemet, and L. Loron, Interconnected Observers for online supercapacitor ageing monitoring, 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, nov. 2013, p. 6746-6751.

Z. Bououchma, and J. Sabor, Online diagnosis of supercapacitors using extended Kalman filter combined with PID corrector, International Journal of Power Electronics and Drive Systems(IJPEDS)Vol.12, No.3, September 2021, pp. 1521 1534

Published
2022-02-08
How to Cite
Bououchma, Z., & Jalal, S. (2022). Comparison Between Recursive Least Squares Method and Kalman Filter for Online Identification of Supercapacitor State of Health. Statistics, Optimization & Information Computing, 10(1), 119-134. https://doi.org/10.19139/soic-2310-5070-1195
Section
Research Articles