Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis

Real-time lithium plating detection is essential for ensuring lithium-ion batteries’ safety and longevity. While impedance analysis offers valuable insights into the lithium plating process, the plating detection efficacy is often limited by overlapping electrochemical-thermal phenomena, complicating the extraction of plating-specific impedance features. To overcome this challenge, this work proposes a dynamic impedance tracking framework based on a specifically designed equivalent circuit model (ECM), enabling real-time observation of interfacial electrochemical dynamics. An adaptive lithium plating detection framework further enhances accuracy by employing statistical thresholding to distinguish plating-induced impedance variations from normal operational fluctuations. A consistency metric is formulated to quantitatively assess the proposed method’s performance across diverse charging rates and thermal conditions. Experimental validation demonstrates the proposed method’s superior sensitivity and robustness compared to three conventional impedance-based plating indicators. • Dynamic impedance tracking enabling real-time lithium plating detection during battery charging process. • A customized equivalent circuit model allowing continuous observation of interfacial electrochemical behavior. • Abnormal charge-transfer resistance drops detection via statistical thresholding. • A consistency metric evaluating plating detection under variable conditions.

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Publication Details

Journal
Applied Energy
Published
2025-10-15
DOI
https://doi.org/10.1016/j.apenergy.2025.126810
Citations
3
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
2.05
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article

Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis

Fei Gao, Yassine Amirat, Jinhao Meng, Mohamed Benbouzid et al.
3 citations
Applied Energy
Advanced Battery Technologies Research
2.05
article

Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis

Fei Gao, Yassine Amirat, Jinhao Meng, Mohamed Benbouzid, Xinghao Du
article en
3 citations

Abstract

Real-time lithium plating detection is essential for ensuring lithium-ion batteries’ safety and longevity. While impedance analysis offers valuable insights into the lithium plating process, the plating detection efficacy is often limited by overlapping electrochemical-thermal phenomena, complicating the extraction of plating-specific impedance features. To overcome this challenge, this work proposes a dynamic impedance tracking framework based on a specifically designed equivalent circuit model (ECM), enabling real-time observation of interfacial electrochemical dynamics. An adaptive lithium plating detection framework further enhances accuracy by employing statistical thresholding to distinguish plating-induced impedance variations from normal operational fluctuations. A consistency metric is formulated to quantitatively assess the proposed method’s performance across diverse charging rates and thermal conditions. Experimental validation demonstrates the proposed method’s superior sensitivity and robustness compared to three conventional impedance-based plating indicators. • Dynamic impedance tracking enabling real-time lithium plating detection during battery charging process. • A customized equivalent circuit model allowing continuous observation of interfacial electrochemical behavior. • Abnormal charge-transfer resistance drops detection via statistical thresholding. • A consistency metric evaluating plating detection under variable conditions.

Applied EnergyVol. 402
Centre National de la Recherche Scientifique (FR), Université de Bretagne Occidentale (FR), Institut Supérieur de l'Électronique et du Numérique (FR), Université de technologie de belfort-montbéliard (FR), Institut de Recherche Dupuy de Lôme (FR), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 12%
Advanced Battery Technologies Research
2.05
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