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An adaptive fractional-order unscented Kalman filter for Li-ion batteries in the energy storage system.

Chen, L.; Shunli, W.; Jiang, H.; Fernandez, C.

Authors

L. Chen

W. Shunli

H. Jiang



Abstract

Accurate estimation of the state of charge (SOC) can prolong the working life and enhance the safety of energy storage system. Considering the influence of noise and parameter changes in the operating environment, an adaptive fractional-order unscented Kalman filter algorithm is introduced to strengthen the accuracy of SOC estimation. To verify the effectiveness and robustness of the algorithm, the simulation is carried out under UDDS complex conditions. The experimental results indicate that the proposed algorithm has the highest SOC precision among the four algorithms, and the RMSE is 1.37%, indicating the superiority of the fractional-order modeling and the joint estimation algorithm. The online identification of full parameters can solve the shortcoming of the long time to obtain the open-circuit voltage in the experiment, and the adaptive filtering algorithm can overcome the problem of filtering divergence and improve the flexibility of SOC estimation.

Citation

CHEN, L., SHUNLI, W., JIANG, H. and FERNANDEZ, C. 2022. An adaptive fractional-order unscented Kalman filter for li-ion batteries in the energy storage system. Indian journal of physics [online], 96(13), pages 3933-3939. Available from: https://doi.org/10.1007/s12648-022-02314-2

Journal Article Type Article
Acceptance Date Feb 3, 2022
Online Publication Date Mar 12, 2022
Publication Date Nov 30, 2022
Deposit Date Apr 22, 2022
Publicly Available Date Mar 13, 2023
Journal Indian Journal of Physics
Print ISSN 0019-5480
Electronic ISSN 0974-9845
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 96
Issue 13
Pages 3933-3939
DOI https://doi.org/10.1007/s12648-022-02314-2
Keywords Li-ion battery; State of charge; Adaptive fractional-order unscented Kalman filter; Energy storage system; Residual sequence
Public URL https://rgu-repository.worktribe.com/output/1628631

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Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s12648-022-02314-2





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