Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of m...

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Published in	IEEE transactions on industrial electronics (1982) Vol. 68; no. 1; pp. 312 - 323
Main Authors	Wei, Zhongbao, Dong, Guangzhong, Zhang, Xinan, Pou, Josep, Quan, Zhongyi, He, Hongwen
Format	Journal Article
Language	English
Published	New York IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Adaptation models Algorithms Batteries Battery management bias compensation Current measurement Lithium-ion batteries Mathematical models model identification Noise Noise measurement Observers Parameter estimation Parameter identification Parameterization Rechargeable batteries State of charge state of charge (SOC)
Online Access	Get full text
ISSN	0278-0046 1557-9948 1557-9948
DOI	10.1109/TIE.2019.2962429

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Abstract	Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.
AbstractList	Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.
Author	Quan, Zhongyi Dong, Guangzhong Zhang, Xinan Pou, Josep He, Hongwen Wei, Zhongbao
Author_xml	– sequence: 1 givenname: Zhongbao orcidid: 0000-0003-0051-5648 surname: Wei fullname: Wei, Zhongbao email: weizb@bit.edu.cn organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 2 givenname: Guangzhong orcidid: 0000-0002-0757-8580 surname: Dong fullname: Dong, Guangzhong email: dong@chalmers.se organization: Department of Electrical Engineering, Chalmers University of Technology, Göteborg, Sweden – sequence: 3 givenname: Xinan orcidid: 0000-0002-9472-8785 surname: Zhang fullname: Zhang, Xinan email: xinan.zhang@uwa.edu.au organization: University of Western Australia, Perth, WA, Australia – sequence: 4 givenname: Josep orcidid: 0000-0002-3114-781X surname: Pou fullname: Pou, Josep email: josep.pou@ieee.org organization: School of Electrical and Electronic Engineering, Nanyang Technology University, Singapore, Singapore – sequence: 5 givenname: Zhongyi orcidid: 0000-0001-5612-0296 surname: Quan fullname: Quan, Zhongyi email: zquan@ualberta.ca organization: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada – sequence: 6 givenname: Hongwen orcidid: 0000-0003-2874-1858 surname: He fullname: He, Hongwen email: hwhebit@bit.edu.cn organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
BackLink	https://research.chalmers.se/publication/521017$$DView record from Swedish Publication Index
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Snippet	Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely...
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SubjectTerms	Adaptation models Algorithms Batteries Battery management bias compensation Current measurement Lithium-ion batteries Mathematical models model identification Noise Noise measurement Observers Parameter estimation Parameter identification Parameterization Rechargeable batteries State of charge state of charge (SOC)
Title	Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization
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