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

Paper:

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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

Received:
March 18, 2016
Accepted:
May 12, 2016
Published:
November 20, 2016
Creative Commons License
Keywords:
electric vehicle (EV), li-ion battery, battery model, SOC estimation, kalman filter
Abstract
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.
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Last updated on Oct. 01, 2024