Lithium-Ion Batteries SOH Estimation With Multimodal Multilinear Feature Fusion

Lin, Mingqiang; You, Yuqiang; Meng, Jinhao; Wang, Wei; Wu, J.; Stroe, Daniel‐Ioan

doi:10.1109/tec.2023.3282017

Cited by 24 publications

(1 citation statement)

References 44 publications

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“…Based on the correlation coefficient, features with a higher correlation can be selected for preservation, while features with a lower correlation can be ignored for feature dimensionality reduction. When the correlation coefficient between two samples exceeds 0.5, it indicates a relatively strong correlation [30]. From the thermal map, we observe that the absolute values of the correlation between R 0 and C cpe , R 1 and α, and α and β are 0.7231, 0.7314, and 0.5224, respectively.…”

Section: Clustering-based Battery Classificationmentioning

confidence: 93%

Classification of Lithium-Ion Batteries Based on Impedance Spectrum Features and an Improved K-Means Algorithm

Zhang,

Tian,

Yan

et al. 2023

Batteries

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This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is based on the flow direction algorithm (FDA). In order to reduce the dimensionality of battery features, the Pearson correlation coefficient is employed to analyze the correlation between impedance spectrum features. The battery classification is carried out using the improved K-means algorithm, which incorporates the optimization of the initial clustering center using the grey wolf optimization (GWO) algorithm. The experimental results demonstrate the effectiveness of this method in accurately classifying batteries and its high level of accuracy and robustness. Consequently, this method can be relied upon to provide robust support for battery performance evaluation and fault diagnosis.

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Section: Clustering-based Battery Classificationmentioning

confidence: 93%