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. |
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| 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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| 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] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19092227 |
