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,以便快速评估退役电动汽车电池是否保留用于二次利用,从而超出原始和首要意图,或将其送往回收设施。所提出的算法使用简单统计方法从可用的电池电流和电压测量生成特征,使用相关分析选择和排名特征,并采用增强的带袋高斯过程回归。该方法在公开可用的老化数据集上进行验证,其中包括200多个具有不同阴极化学成分以及不同操作条件下的慢充电和快充电的电池。多个训练-测试分区的结果表明,最坏情况下的均方根百分比误差和平均百分比误差性能误差的平均值分别小于1.48%和1.29%,获得了良好的结果。