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Online Measured Impedance-Assisted State-of-Charge Estimation for Lithium-Ion Batteries Under Low Excitation Conditions via Fractional-Order Modeling.

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Title: Online Measured Impedance-Assisted State-of-Charge Estimation for Lithium-Ion Batteries Under Low Excitation Conditions via Fractional-Order Modeling.
Authors: Chen, Zheng1 (AUTHOR), Li, Yanlong2 (AUTHOR), Liu, Chaohou2,3 (AUTHOR), Wu, Yuying1,4 (AUTHOR), Wang, Lei1,3 (AUTHOR), Yao, Yousu2,4 (AUTHOR), Li, Jian1,3 (AUTHOR) leejian@uestc.edu.cn
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2227. 24p.
Subject Terms: *Impedance spectroscopy, *Parameter estimation, *Battery management systems, *Lithium-ion batteries, *Kalman filtering, *Fractional calculus
Abstract: Accurate online parameter identification and state-of-charge (SOC) estimation are essential for lithium-ion battery management systems. However, under constant or quasi-constant current operating conditions, the system excitation is inherently weak, leading to poor parameter identifiability when conventional model-based estimation methods are used. This issue is particularly critical in grid-connected battery energy storage systems, where current dynamics are limited. To address this problem, this paper proposes an online measured impedance-assisted SOC estimation framework that integrates online electrochemical impedance measurements with a fractional-order battery model and an extended Kalman filter. Online impedance data are utilized to update the model parameters in real time through a geometric-based fitting algorithm, thereby enhancing model adaptability under low excitation conditions. Experimental results obtained from lithium-ion cells with different aging states demonstrate that the proposed method enables stable and accurate online parameter identification and SOC estimation under the tested low-excitation conditions, where conventional time-domain approaches tend to degrade or diverge. Robustness under highly dynamic operating conditions remains to be further validated. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193716123
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Online Measured Impedance-Assisted State-of-Charge Estimation for Lithium-Ion Batteries Under Low Excitation Conditions via Fractional-Order Modeling.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Zheng%22">Chen, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yanlong%22">Li, Yanlong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chaohou%22">Liu, Chaohou</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yuying%22">Wu, Yuying</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Lei%22">Wang, Lei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Yousu%22">Yao, Yousu</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jian%22">Li, Jian</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> leejian@uestc.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2227. 24p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Impedance+spectroscopy%22">Impedance spectroscopy</searchLink><br />*<searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Battery+management+systems%22">Battery management systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink><br />*<searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br />*<searchLink fieldCode="DE" term="%22Fractional+calculus%22">Fractional calculus</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate online parameter identification and state-of-charge (SOC) estimation are essential for lithium-ion battery management systems. However, under constant or quasi-constant current operating conditions, the system excitation is inherently weak, leading to poor parameter identifiability when conventional model-based estimation methods are used. This issue is particularly critical in grid-connected battery energy storage systems, where current dynamics are limited. To address this problem, this paper proposes an online measured impedance-assisted SOC estimation framework that integrates online electrochemical impedance measurements with a fractional-order battery model and an extended Kalman filter. Online impedance data are utilized to update the model parameters in real time through a geometric-based fitting algorithm, thereby enhancing model adaptability under low excitation conditions. Experimental results obtained from lithium-ion cells with different aging states demonstrate that the proposed method enables stable and accurate online parameter identification and SOC estimation under the tested low-excitation conditions, where conventional time-domain approaches tend to degrade or diverge. Robustness under highly dynamic operating conditions remains to be further validated. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/en19092227
    Languages:
      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 2227
    Subjects:
      – SubjectFull: Impedance spectroscopy
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Battery management systems
        Type: general
      – SubjectFull: Lithium-ion batteries
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Fractional calculus
        Type: general
    Titles:
      – TitleFull: Online Measured Impedance-Assisted State-of-Charge Estimation for Lithium-Ion Batteries Under Low Excitation Conditions via Fractional-Order Modeling.
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            NameFull: Chen, Zheng
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            NameFull: Li, Yanlong
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            NameFull: Liu, Chaohou
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            NameFull: Wu, Yuying
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            NameFull: Wang, Lei
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19
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              Value: 9
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            – TitleFull: Energies (19961073)
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