The rapid deployment of lithium-ion batteries in clean energy and electric vehicle applications will also increase the volume of retired batteries in the coming years. Retired Li-ion batteries could have residual capacities up to 70–80% of the nominal capacity of a new battery, which could be lucrative for a second-life battery market, also creating environmental and economic benefits. Presently, retired batteries are first screened to select usable batteries and then a proper secondary application is choosen according to the battery performance. Here, a complete process for grouping used batteries is proposed including safety checking, performance evaluation, data processing, and clustering of batteries. Also, a novel clustering algorithm of retired batteries based on traversal optimization is proposed. The new method does not require defining the cluster numbers and centers in beforehand, but possesses immunity to outliers. It can be used both for small and large sample sizes, as the optimization parameters used do not require iteration. The Davies-Bouldin Index of the proposed algorithm shows that the greatest differences are found between clusters, but the least differences between the samples within a single cluster, which indicates the effectiveness of the algorithm.

锂离子电池在清洁能源和电动汽车应用中的快速部署也将在未来几年内增加已淘汰电池的数量。淘汰的锂离子电池的剩余容量可能高达新电池标称容量的70–80％，这对于第二生命周期的电池市场可能是有利可图的，还可以带来环境和经济利益。目前，首先要筛选淘汰的电池以选择可用的电池，然后根据电池性能选择适当的二次应用。在此，提出了用于对废旧电池进行分组的完整过程，包括安全检查，性能评估，数据处理和电池组。同时，提出了一种基于遍历优化的退役电池聚类算法。新方法不需要事先定义聚类数和中心，但是具有对异常值的免疫力。它可以用于大小样本，因为所使用的优化参数不需要迭代。该算法的Davies-Bouldin索引表明，聚类之间的差异最大，而单个聚类中的样本之间的差异最小，这表明了该算法的有效性。