Presentation of a Mix Algorithm for Estimating the Battery State of Charge Using Kalman Filter and Neural Networks

Authors: Amin Sedighfar, M. R. Moniri

Abstract:

Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate. 

Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316249

References:

[1] Wen-Yean Chang “The State of Charge Estimating Methods for Battery: A Review” Hindawi Publishing Corporation, Article ID 953792, 7 pages, Volume 2013.
[2] Goran Kujundzicm Mario Vasak, Jadranko Matusko “Estimation of VRLA Battery States and Parameters Using Sigma-Point Kalman Filter” International Conference on Electrical Drivers and Power Electronics, pp. 204-211, 21-23Sep 2015.
[3] Guo Xiangwei, Kang Longyun, Huang Zhizhen “On-Line State of Charge Estimation of Lithium-Ion Power Battery Pack Using Optimized Unscented Kalman Filtering” ITEC Asia-Pacific, 2014.
[4] John Chiasson, Baskar Vairamohan “Estimating the State of Charge of a battery” IEEE Control System Society, April 2005.
[5] H. Rahimi-Eichi, F. Barnoti, M.Y. Chow “Modeling and Online Parameters Identification of Li-Polymer Battery Cells for SOC Estimation” Industrial Electronics (ISIE), IEEE International Symposium, 2012.
[6] Koray Kutluay, Yigit Cadirei, Yakup S. Ozkazanc “A New Online State-of-Charge Estimation and Monitoring System for Sealed Lead-Acid Batteries in Telecommunication Power Supplies” IEEE Transactions on Industrial Electronics, Volume 52, No 5, pp. 1315-1327, Oct 2005.
[7] D. Linden, T. B Reddy, Handbook of Batteries, Third Edition, McGraw Hill, 2001.
[8] M. Galad, P. Spanik, M. Cacciato, G. Nobile “Comparison of Common and Combined State of Charge Estimation Methods for VRLA Batteries” Published in Elektro, pp. 220-225, 2016.
[9] Micheal Wahlstorm, Design of a Battery State Estimator Using a Dual Extended Kalman Filter, a MS Thesis Presented to University of Waterloo, pp. 45-47, 2010.
[10] Juan Carlos Alvarez Anton, Paulino Jose Garcia Nieto, Celcilio Blanco Viejo, Jose Antonio Vilan Vilan “Support Vector Machines Used to Estimate the Battery State of Charge” IEEE Transaction on Power Electronics, Volume 28, No 12, pp. 5919-5926, Dec 2013.
[11] T. Hansen, C. J Wang, “Support Vector Based Battery State of Charge Estimator” Journal of Power Sources, Vol. 141, No.2, pp. 351-358, 2005.
[12] Renjian Feng, Shuai Zhao, Xiaodong Lu “On-Line Estimation of Dynamic State-of-Charge for Lead Acid Battery Based on Fuzzy Logic” 2nd International Conference on Measurement Information and Control, China, pp. 447-451, Aug 2013.
[13] Mohammad Charkhgard, Mohammad Farrokhi “State of Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF” IEEE Transaction on Industrial Electronics, Volume 57, No 12, pp. 4178-4187, Dec 2010.
[14] B. S Bhangu, P. Bentley, D. A. Stone, C. M. Bingham “Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles” IEEE Transaction on Vehicular Technology, Volume 54. No 3, pp. 783-794, May 2005.
[15] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edition, Englewood Cliffs, Prentice-Hall, 1999.