Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81519
Title: LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering
Authors: Xu, Zhu
Gao, Shibin
Yang, Shunfeng
Keywords: Batteries
Self organized systems
Issue Date: 2016
Source: Xu, Z., Gao, S., & Yang, S. (2016). LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering. Journal of Renewable and Sustainable Energy, 8(2), 024103-.
Series/Report no.: Journal of Renewable and Sustainable Energy
Abstract: 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 LiFePO4battery, 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.
URI: https://hdl.handle.net/10356/81519
http://hdl.handle.net/10220/40830
ISSN: 1941-7012
DOI: 10.1063/1.4944335
Rights: © 2016 AIP Publishing LLC. This paper was published in Journal of Renewable and Sustainable Energy and is made available as an electronic reprint (preprint) with permission of AIP Publishing LLC. The published version is available at: [http://dx.doi.org/10.1063/1.4944335]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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