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 LiFePO4 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.

中文翻译：

---

基于模糊C-Means算法的报废锂离子电池二次利用快速筛选方法


随着大量锂离子电池从电动汽车中退役，电池二次利用的研究迫在眉睫。然而，现有的对大量细胞的筛选方法无法同时保证高效率和高精度。本文提出一种基于改进的模糊c均值(FCM)算法的快速准确的筛选方法。首先，基于增量容量分析来优化选择每个单体电池的部分充电曲线，这通常用于检测电池老化机制。其次，从部分充电曲线中提取四个重要特征，包括关键点、曲线梯度、电压能量和波动性。此外，特征优化是通过观察容量和特征之间的关系来完成的。最后，利用改进的FCM算法对退役电池进行最优特征筛选。对176个LiFePO4电池的筛选结果证明了该方法的高精度和高效率。与支持向量机和神经网络方法相比，该方法具有更好的通用性和更高的效率，无需预处理训练。筛选准确率可达90.9%。在允许误差为 1% 的情况下，它可以高达 95.5%。筛选效率比监督筛选方法高约7.6倍。