LAPSE:2023.29448
Published Article

LAPSE:2023.29448
Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy
April 13, 2023
Abstract
In the battery management system, it is important to accurately and efficiently estimate the state of charge (SOC) of lithium-ion batteries, which generally requires the establishment of a equivalent circuit model of the battery, whose accuracy and rationality play an important role in accurately estimating the state of lithium-ion batteries. The traditional single order equivalent circuit models do not take into account the changes of impedance spectrum under the action of multiple factors, nor do they take into account the balance of practicality and complexity of the model, resulting the low accuracy and poor practicability. In this paper, the theory of electrochemical impedance spectroscopy is used to guide and improve the equivalent circuit model. Based on the analysis of the variation of the high and intermediate frequency range of the impedance spectrum with the state of charge and temperature of the battery, a variable order equivalent model (VOEM) is proposed by Arrhenius equation and Bayesian information criterion (BIC), and the state equation and observation equation of VOEM are improved by autoregressive (AR) equations. Combined with the unscented Kalman filter (UKF), a SOC online estimation method is proposed, named VOEM-AR-UKF. The experimental results show that the proposed method has high accuracy and good adaptability.
In the battery management system, it is important to accurately and efficiently estimate the state of charge (SOC) of lithium-ion batteries, which generally requires the establishment of a equivalent circuit model of the battery, whose accuracy and rationality play an important role in accurately estimating the state of lithium-ion batteries. The traditional single order equivalent circuit models do not take into account the changes of impedance spectrum under the action of multiple factors, nor do they take into account the balance of practicality and complexity of the model, resulting the low accuracy and poor practicability. In this paper, the theory of electrochemical impedance spectroscopy is used to guide and improve the equivalent circuit model. Based on the analysis of the variation of the high and intermediate frequency range of the impedance spectrum with the state of charge and temperature of the battery, a variable order equivalent model (VOEM) is proposed by Arrhenius equation and Bayesian information criterion (BIC), and the state equation and observation equation of VOEM are improved by autoregressive (AR) equations. Combined with the unscented Kalman filter (UKF), a SOC online estimation method is proposed, named VOEM-AR-UKF. The experimental results show that the proposed method has high accuracy and good adaptability.
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Keywords
Bayesian information criterion, electrochemical impedance spectroscopy, equivalent circuit model, lithium-ion battery, state of charge
Subject
Suggested Citation
Zhang J, Wang P, Liu Y, Cheng Z. Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy. (2023). LAPSE:2023.29448
Author Affiliations
Zhang J: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Wang P: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China [ORCID]
Liu Y: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Cheng Z: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Wang P: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China [ORCID]
Liu Y: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Cheng Z: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Journal Name
Energies
Volume
14
Issue
3
First Page
769
Year
2021
Publication Date
2021-02-01
ISSN
1996-1073
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Original Submission
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PII: en14030769, Publication Type: Journal Article
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LAPSE:2023.29448
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https://doi.org/10.3390/en14030769
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