An enhanced sorting framework for retired batteries based on multi-dimensional features and an integrated clustering approach

Retired batteries for secondary use offer significant economic benefits and environmental value. Accurate sorting of retired batteries with diverse characteristics can further enhance their application efficiency. However, in practical sorting processes, the presence of redundant features, noise interference, and distribution discrepancies in the data severely limits the accuracy of sorting outcomes. To address these challenges, this paper proposes an enhanced retired battery sorting strategy that incorporates feature selection and a clustering algorithm, aiming to optimize the sorting process from the perspective of feature data. To address feature redundancy and high dimensionality issues, this paper proposes an entropy screening method. The Local Outlier Factor algorithm is used to remove anomalous samples. Subsequently, an ensemble clustering approach is developed based on K-means, Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Model, and Spectral clustering, to handle diverse data distributions. The proposed method is validated on 100 retired batteries as well as the large-scale dataset. Additionally, its strong sorting capability and engineering applicability are further demonstrated through carefully designed aging-controlled experiments.