Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144528
Title: Online estimation of power capacity with noise effect attenuation for lithium-ion battery
Authors: Wei, Zhongbao
Zhao, Jiyun
Xiong, Rui
Dong, Guangzhong
Pou, Josep
Tseng, King Jet
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Wei, Z., Zhao, J., Xiong, R., Dong, G., Pou, J., & Tseng, K. J. (2019). Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery. IEEE Transactions on Industrial Electronics, 66(7), 5724–5735. doi:10.1109/tie.2018.2878122
Journal: IEEE Transactions on Industrial Electronics
Abstract: Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.
URI: https://hdl.handle.net/10356/144528
ISSN: 0278-0046
DOI: 10.1109/TIE.2018.2878122
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIE.2018.2878122.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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