A Quick Screening Approach Based on Fuzzy C-Means Algorithm for the Second Usage of Retired Lithium-Ion Batteries

Abstract

With numerous lithium-ion batteries retired from electric vehicles, the studies on the battery second usage are extremely imminent. However, existing screening approaches on plenty of cells fail to guarantee high efficiency and high accuracy simultaneously. This article proposes a quick and accurate screening method based on the improved fuzzy c-means (FCM) algorithm. First, the partial charging curve of every single cell is selected optimally based on the incremental capacity analysis, which is frequently used to detect the battery aging mechanism. Second, four important features are extracted from the partial charging curves, including key point, curve gradient, voltage energy, and volatility. Furthermore, feature optimization is done by observing the relationship between capacity and feature. Finally, the retired batteries are screened with the optimal features using the improved FCM algorithm. The screening result on 176 LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> batteries proves the high accuracy and high efficiency of the approach. Compared with the support vector machine and neural network approaches, the proposed method has better generality and higher efficiency without pretreatment training. The screening accuracy can reach 90.9%. With a permitted error of 1%, it can be as high as 95.5%. The screening efficiency is about 7.6 times higher than the supervised screening method.