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首页> 外文期刊>Journal of Energy Storage >Multivariate statistical analysis based cross voltage correlation method for internal short-circuit and sensor faults diagnosis of lithium-ion battery system
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Multivariate statistical analysis based cross voltage correlation method for internal short-circuit and sensor faults diagnosis of lithium-ion battery system

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摘要

Extracting fault signatures within battery packs using voltage correlations rather than voltage measurements has been shown to be an effective way to overcome cell inconsistencies, and the correlation coefficient (CC) is a highly desirable tool for such a methodology. However, current CC-based research advances have focused only on the analysis of a single CC signal at a time, which is clearly very inefficient for lithium-ion battery systems (LIBS) with a large number of cells. In addition, the existing results still have some shortcomings in terms of window-width selection and multi-fault identification. To address these issues, this paper proposes an independent component analysis (ICA) and principal component analysis (PCA) based scheme with cross -cell sensor topology for fast and accurate diagnosis of internal short-circuit and sensor faults in LIBS, where ICA-PCA based diagnostic model is used for parallel monitoring of high-dimensional non-Gaussian CC signals, while the cross-cell sensor topology is used for the identification of fault types. In particular, a semi-quantitative merit-seeking criterion based on kernel density estimation is proposed for choosing the optimal window-width, and a multi-fault identification logic based on improved contribution plot and cross-cell sensor topology is given to intuitively determine the fault type and the location of the failed cell/sensor. Experimental results on a real battery test platform show that the proposed method significantly outperforms existing CC-based methods in terms of fault detection latency, fault detection rate, and diagnostic intuitiveness.

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