论文标题
使用机器学习评估电池对二人应用的可行性
Evaluating feasibility of batteries for second-life applications using machine learning
论文作者
论文摘要
本文介绍了机器学习技术的组合，以迅速评估退休的电动汽车电池，以便将这些电池保留在第二寿命应用中，并将其运行范围扩展到原始和第一次意图之外，或者将其发送到回收设施。所提出的算法从可用的电池电流和使用简单统计数据的电压测量值中生成功能，使用相关分析选择并对功能进行排名，并采用高斯过程回归，并随着行李的方式增强了功能。该方法在具有缓慢充电，不同的阴极化学和不同操作条件的200多个单元格的公开老化数据集中得到了验证。根据多个训练测试分区观察到有希望的结果，其中均方根平方误差和平均百分比误差性能误差分别小于1.48％和1.29％，在最坏情况下。
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.
