A two-stage sorting method combining static and dynamic characteristics for retired lithium-ion battery echelon utilization

The battery echelon utilization is to sort and reuse the retired lithium-ion batteries with poor consistency, which puts forward higher requirements on how to guarantee their comprehensive consistencies after sorting. To address this issue, we combine static and dynamic characteristics as discharge capacity, temperature rise and voltage curves, and propose a two-stage sorting method for retired batteries. In the first stage, taking the discharge capacity and the temperature rise as initial sorting characteristics, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for preliminary sorting that eliminates the abnormal batteries and obtains the possible cluster number K. In the second stage, the cluster number K leads to the reference range of the initial value of K-means++ algorithm, and the dynamic features extracted from voltage curves by t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm are as the input of it. The class labels of retired batteries are obtained after clustering calculation. Lastly, the validity and accuracy of the proposed method are verified based on NASA public data set, and four typical clustering evaluation indexes are applied to show its advantages. The retired batteries within the same class not only have remarkable comprehensive consistency in the three selected characteristics, but also have the consistent aging degree so that the service life of retired batteries can be prolonged to a great extent.