JACIII Vol.20 No.6 pp. 861-866
doi: 10.20965/jaciii.2016.p0861


Simulation Study on Battery State of Charge Estimation Using Kalman Filter

Furqan Asghar*,†, Muhammad Talha*, Sung Ho Kim**, and In-Ho Ra***

*School of Electronics and Information Engineering, Kunsan National University

**Department of Control and Robotics Engineering, Kunsan National University

***Department of Telecommunication Engineering, Kunsan National University
558, Daehak-ro, Gunsan-si, Jeollabuk-do, South Korea

Corresponding author

March 18, 2016
May 12, 2016
November 20, 2016
electric vehicle (EV), li-ion battery, battery model, SOC estimation, kalman filter
Low power dissipation and maximum battery run-time are crucial in portable electronics and EV’s. Battery characteristics and performance varied at different operating conditions. By using accurate, efficient circuit and battery models, designers can predict and optimize battery runtime, current state of charge (SOC) and circuit performance. A great factor in determining the stability of battery system lies within the state of charge estimation. Failing to predict SOC will cause overcharge or over discharge which potentially will bring permanent damage to the battery cells. Open circuit voltage (OCV) has been widely used to estimate the state of charge in estimation algorithms. This paper proposed an accurate and comprehensive battery state of charge (SOC) estimation method by using the Kalman filter. First, Kalman filter for Li-ion battery state of charge estimation was mathematically designed. Then Electrical battery model is being implemented with Kalman filter in matlab Simulink to estimate the exact battery state of charge using estimated battery open circuit voltages. The proposed model shows that system is estimating battery state of charge more accurately than commonly used methods which can help to improve battery performance and lifetime.
Cite this article as:
F. Asghar, M. Talha, S. Kim, and I. Ra, “Simulation Study on Battery State of Charge Estimation Using Kalman Filter,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 861-866, 2016.
Data files:
  1. [1] S. Piller, M. Perrin, and A. Jossen, “Methods for state-of-charge determination and their applications,” J. Power Sources, Vol.96, pp. 113-120, 2001.
  2. [2] W. Y. Chang, “The State of Charge Estimation Methods for Battery: A Review,” ISRN Applied Mathematics, Vol.13, 2013.
  3. [3] K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries,” Applied Energy, Vol.86, pp. 1506-1511, 2009.
  4. [4] M. Shahriari and M. Farrokhi, “State-of-Charge Estimation of VRLA Batteries using Neural Networks and Extended Kalman Filter,” IFAC Workshop on Intelligent Control Systems, Vol.43, pp. 52-56, 2010.
  5. [5] M. Chen and G. A. Rincon-Mora, “Accurate Electrical Battery Model capable of Predicting runtime and I-V Performance,” IEEE Trans. on Energy Conversion, Vol.21, June 2006.
  6. [6] R. C. Kroeze and P. T. Krein, “Electrical Battery Model for use in dynamic Electric Vehicle Simulations,” Power Electronics Specialist’s Conf., pp. 1336-1342, June 2008.
  7. [7] M. Daowd, N. Omar, B. Verbrugge, P. V. D. Bossche, and J. V. Mierlo, “Battery Models Parameters Estimation based on Matlab/Simulink,” 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symp. & Exhibition, November 2010.
  8. [8] C. Jiang, A. Taylor, C. Duan, and K. H. Bai, “Extended Kalman Filter Based Battery State of Charge Estimation for Electric Vehicles,” Transportation Electrification Conf. and Expo (ITEC), June 2013.
  9. [9] S.-Y. Kim, F. Asghar, and S. H. Kim. “Battery Open Circuit Voltage Estimation using Kalman Filter,” Proc. of the KIIS Spring Conf., Vol.25, No.1. April 2015.
  10. [10] O. Nam, J. Lee, J. Lee, J. Kim, and B. H. Cho, “ Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering,” J. of Power Sources, Vol.174, pp. 9-15, November, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024