A Minor Fault Diagnosis Method for Lithium-Ion Batteries Based on the Fusion of Lumped Electrochemical Model and Grubbs Statistical Test

Le résumé fourni par la source

Timely fault diagnosis is crucial for ensuring the reliable and safe operation of lithium-ion batteries (LIBs). Traditional threshold-based approaches often miss gradual or subtle faults, resulting in false alarms or misdiagnoses due to data noise. This paper presents an advanced fault detection method that effectively integrates a lumped electrochemical model with the Grubbs statistical test. This combination allows for accurate identification of faults at the cell level, significantly improving the detection of potential faults. Specifically, the lumped electrochemical model is used to predict the voltage of the cells. Residuals are calculated by comparing the experimentally obtained measurements with the predicted values. These residuals are analyzed using the Grubbs test to identify statistically significant differences that may indicate the presence of potential faults. The effectiveness and suitability of the proposed method for real-world applications were demonstrated using the Urban Dynamometer Driving Schedule (UDDS). This presented technology has achieved 96.61% fault detection rate and 96.53% detection accuracy rate. These findings demonstrate that the combination of statistical and model-based approaches is highly effective and holds significant promise as an online condition monitoring tool for LIB systems.

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