Paper Publications

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Impact Factor:6.6

DOI number:10.1109/TPEL.2025.3555773

Teaching and Research Group:G. Liu, Z. Gao, M. Lin, X. Liu, & J. Wu

Journal:IEEE Transactions on Power Electronics

Key Words:Fault diagnosis, internal short circuit, model-free approach, sodium-ion batteries

Abstract:Thermal runaway caused by the internal short circuit (ISC) poses a significant safety risk for sodium-ion batteries (SIBs) in electric vehicles and energy storage applications. Early detection of ISC faults is considered a potential way to reduce the risk associated with fire or explosion and can be identified via minor power leakage. However, diagnosing these leaks in the initial stages of ISC is quite challenging without battery modeling or preliminary experiments. Here, a model-free method based on charging capacity difference analysis is proposed for quantitatively diagnosing ISC fault. Firstly, a wavelet denoising algorithm with improved thresholding is presented to extract the real signals from the noise-containing voltage and current data. Subsequently, the ISC resistance is estimated by calculating the charging capacity difference variation between the ISC cell and the normal cell. Furthermore, the amount of power leakage during the charging process of ISC batteries is considered to improve the accuracy of the ISC diagnosis. Finally, experiments with elaborate ISC batteries are conducted to validate the method, achieving a resistance estimation error below 5% across different ISC severities while maintaining robustness to capacity inconsistencies. Compared to existing model-free methods, the proposed method significantly enhances diagnostic accuracy and broadens its applicability.

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Page Number:Early Access

Translation or Not:no

Date of Publication:2025-03-28

Included Journals:SCI

Links to published journals:https://ieeexplore.ieee.org/document/10945515