Joint Estimation of SOC and SOH for Lithium Batteries based on DEKF
Pages 1016 - 1020
Abstract
The accuracy of estimation of SOC and SOH of lithium battery can affect the control strategy of the electric vehicle battery management system, which affects the life of the battery. The paper uses equivalent circuit to simulate the model of battery, and adopt the Least Square (LS) method to recognize the parameters of model. Conduct the experiment on battery by the mix of charged and discharge condition, use the state Kalman Filter (KF) to estimate SOC of battery. Use Parameter Klaman Filter and the Least Square Method with forgetting factor respectively to estimate the resistance of battery in order to estimate of SOH of battery. The fitting function of OCV-SOC adopts Gaussian function. The paper verifies that estimation of battery by Kalman Filter doesn't rely on the initial value of SOC, also verifies the feasibility of Double Extended Kalman Filter (DEKF).
References
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Zhang Yanxi.SOC prediction system of lithium power battery based on RBF neural network[D].Anhui Normal University,2019.
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Yuhang Chen, Hongjun Xiong, Yi Guo. DEKF-based SOC estimation study for lithium batteries[J]. Journal of Physics: Conference Series,2022, 2263:012-020.
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Index Terms
- Joint Estimation of SOC and SOH for Lithium Batteries based on DEKF
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Published In
September 2022
1094 pages
ISBN:9781450397414
DOI:10.1145/3558819
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 October 2022
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ICCSIE2022
ICCSIE2022: 7th International Conference on Cyber Security and Information Engineering
September 23 - 25, 2022
QLD, Brisbane, Australia
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