Battery terminal height detection

How to analyze battery potential failure data?

Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data.

Can a long-term feature analysis detect and diagnose battery faults?

In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults. Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems.

What is a battery system failure index?

Moreover, the cumulative root-mean-square deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions.

Why is early diagnosis of battery faults important?

Abstract: Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults.

How are battery fault features extracted?

Specifically, the battery fault features are extracted from the incremental capacity (IC) curves, which are smoothed by advanced filter algorithms. Second, principal component analysis (PCA) algorithm is utilized to reduce dimensionality, and the cumulative percent variance (CPV) is to determine the number of significant features.