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Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference.

Yang, Xiaoyong; Wang, Shunli; Takyi-Aninakwa, Paul; Yang, Xiao; Fernandez, Carlos

Authors

Xiaoyong Yang

Shunli Wang

Paul Takyi-Aninakwa

Xiao Yang



Abstract

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model parameters and the SOC due to the unknown nonlinear characteristics caused by noise. However, the impact of sensor and model errors is treated separately. To express the sensor and model uncertainties, a noise bias compensation-equivalent circuit model (NBC-ECM) is proposed, in which sensor noise and model error voltages are employed in the model structure and the SOC estimation process of the lithium-ion battery. For parameter identification, a singular value decomposition-bias compensation recursive least squares (SVD-BCRLS) algorithm is proposed to identify the characteristic micro-parameters of the battery. Then, a moving window adaptive extended Kalman filtering (MWAEKF) algorithm based on window functions is proposed for accurate SOC estimation of lithium-ion batteries. The stability of the model parameters and the reliability of the proposed algorithm in estimating the SOC are evaluated using different noise factors: current and voltage sensor noises of 10 and 50 mA. Using the proposed SVD-BCRLS-MWAEKF algorithm, the maximum SOC error is 1.3%, the root mean square error (RMSE) is 0.3972%, and the mean absolute error (MAE) is 0.2316% using the noise of 0.05 V/A under the hybrid power pulse characterization (HPPC) operating condition. With the same noise value under the Beijing bus dynamic stress test (BBDST) operating condition, the proposed algorithm SOC has a maximum SOC error of 1.57%, an RMSE of 0.5638%, and an MAE of 0.4475%. Under noise interference conditions, estimation is more accurate compared to static conditions, proving that the proposed algorithm can overcome the uncertainties encountered by lithium-ion batteries for real-time BMS applications.

Citation

YANG, X., WANG, S., TAKYI-ANINAKWA, P., YANG, X. and FERNANDEZ, C. 2023. Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference. Journal of energy storage [online], 73(B), article number 108974. Available from: https://doi.org/10.1016/j.est.2023.108974

Journal Article Type Article
Acceptance Date Sep 9, 2023
Online Publication Date Sep 21, 2023
Publication Date Dec 10, 2023
Deposit Date Sep 15, 2023
Publicly Available Date Sep 22, 2024
Journal Journal of energy storage
Print ISSN 2352-152X
Electronic ISSN 2352-1538
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 73
Issue B
Article Number 108974
DOI https://doi.org/10.1016/j.est.2023.108974
Keywords State of charge; Lithium-ion battery; Noise bias compensation-equivalent circuit model; Singular value decomposition-bias compensation recursive least squares; Moving window adaptive Kalman filter
Public URL https://rgu-repository.worktribe.com/output/2079371