This paper presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries as to either retain those batteries for a second-life application and extend their operation beyond the original and first intent or send them to recycle facilities. The proposed algorithm generates features from available battery current and voltage measurements with simple statistics, selects and ranks the features using correlation analysis, and employs Gaussian Process Regression enhanced with bagging. This approach is validated over publicly available aging datasets of more than 200 cells with slow and fast charging, with different cathode chemistries, and for diverse operating conditions. Promising results are observed based on multiple training-test partitions, wherein the mean of Root Mean Squared Percent Error and Mean Percent Error performance errors are found to be less than 1.48% and 1.29%, respectively, in the worst-case scenarios.

翻译：本文提出了一种结合机器学习技术的方法，旨在快速评估退役电动汽车电池是否适合二次利用——即保留这些电池以延长其超出原始设计用途的使用寿命，或将其送往回收设施。该算法通过简单统计方法从可用的电池电流和电压测量数据中生成特征，利用相关性分析进行特征选择与排序，并采用结合装袋策略的高斯过程回归模型。该方法在包含200多个电池单元的公开老化数据集上进行了验证，这些数据涵盖慢速与快速充电、不同正极化学体系以及多种运行条件。基于多种训练-测试划分的评估结果显示，在最坏情况下，均方根百分比误差和平均百分比误差的均值分别低于1.48%和1.29%。