A precise detection method for transient micro short-circuit faults of lithium-ion batteries through signal processing

TL;DR

This paper presents a novel signal processing technique using wavelet transforms to accurately detect transient micro-short circuit faults in lithium-ion batteries, enhancing safety by identifying subtle voltage deviations quickly and reliably.

Contribution

The paper introduces a wavelet-based detection method for TMSC faults in LIBs, demonstrating improved accuracy and robustness over existing techniques.

Findings

Effectively detects micro-faults with 30 mV voltage drops within seconds

Robust against false and hidden faults under varying current loads

Validated through designed fault experiments

Abstract

A specific failure mode designated as transient micro-short circuit (TMSC) has been identified in practical battery systems, exhibiting subtle and latent characteristics with measurable voltage deviations. To further improve the safe use of lithium-ion batteries (LIBs), this letter introduces a novel method for the precise detection of this TMSC faults within LIBs. The method applies the continuous wavelet transform (CWT) to voltage and current signals, followed by the identification of micro-scale anomalies through the analysis of the coherence in the wavelet spectrum at specific frequency. Through designed fault experiments, the effec-tiveness of this method has been verified. Result demon-strates that it can effectively capture micro-faults with a voltage drop as low as 30 mV within just a few seconds. Furthermore, the proposed method is inherently highly robust and is able to effectively detect false faults and hidden faults under varying current loads, which highlights the superiority of this method.