Lithium iron phosphate (LiFePO4) batteries are widely used as power batteries for electric vehicle applications. For safety issues, it is important to estimate the State of Charge (SOC) of a battery accurately. The improved Thevenin equivalent circuit model is established according to the characteristics of the LiFePO4 battery, and the model parameters are identified by experimental testing. Furthermore, a novel algorithm of SOC online estimation is proposed, which combines the open-circuit voltage method, ampere-hour integration, and Kalman filtering. The simulations and experimental results show that the improved Thevenin equivalent circuit model can enhance the accuracy of SOC estimation. This proposed algorithm could estimate the SOC precisely even with inaccurate initial values and current measurement errors and distinguish the performances between the batteries. The performance of the proposed SOC estimation method when the voltage sensor is unavailable has been investigated and presented as well. From the characteristics mentioned above, this novel approach is able to guarantee the reliability and safety of the batteries.
Skip Nav Destination
Article navigation
March 2016
Research Article|
March 29 2016
LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering
Zhu Xu;
Zhu Xu
a)
1School of Electrical Engineering,
Southwest Jiaotong University
, Chengdu 610031, China
Search for other works by this author on:
Shibin Gao;
Shibin Gao
1School of Electrical Engineering,
Southwest Jiaotong University
, Chengdu 610031, China
Search for other works by this author on:
Shunfeng Yang
Shunfeng Yang
2School of Electrical and Electronic Engineering,
Nanyang Technological University
, Singapore 639798, Singapore
Search for other works by this author on:
a)
Electronic mail: [email protected]
J. Renewable Sustainable Energy 8, 024103 (2016)
Article history
Received:
June 23 2015
Accepted:
February 26 2016
Citation
Zhu Xu, Shibin Gao, Shunfeng Yang; LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering. J. Renewable Sustainable Energy 1 March 2016; 8 (2): 024103. https://doi.org/10.1063/1.4944335
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Improving academic–industry collaboration: A case study of UK distribution system operators
Jamie M. Bright, Hilal Ozdemir, et al.
Weather as a driver of the energy transition – present and emerging perspectives of energy meteorology
Marion Schroedter-Homscheidt, Jan Dobschinski, et al.
Machine learning for modern power distribution systems: Progress and perspectives
Marija Marković, Matthew Bossart, et al.
Related Content
SOC estimation for batteries using MS-AUKF and neural network
J. Renewable Sustainable Energy (April 2019)
State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter
J. Renewable Sustainable Energy (March 2019)
LiFePO4 battery charging strategy design considering temperature rise minimization
J. Renewable Sustainable Energy (December 2017)
Series-parallel grouping modeling simulation and experimental analysis of zinc-nickel single flow batteries
J. Renewable Sustainable Energy (June 2018)
Adaptive sigma Kalman filter method for state-of-charge estimation based on the optimized battery model
J. Renewable Sustainable Energy (July 2017)