Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.

翻译：准确预测电池寿命对于预防性维护、保修以及改进电芯设计与制造至关重要。然而，制造差异性和与使用相关的退化现象使得寿命预测颇具挑战。本研究探究了基于早期寿命中容量-电压数据所提取的新特征，以预测在充放电倍率及放电深度广泛变化条件下循环使用的电芯寿命。特征提取自循环过程中定期进行的参考性能测试（即低倍率全循环）。早期寿命特征能够捕捉电芯的健康状态以及组件级退化模式的变化速率，其中部分特征与电芯寿命强相关。通过使用包含225个镍锰钴/石墨锂离子电芯在广泛老化条件下生成的新数据集，我们证明对于分布内电芯，使用不超过前15%的数据即可实现寿命预测，平均绝对百分比误差为15.1%。进一步采用分层贝叶斯回归模型进行测试，结果显示外推性能有所提升，对于分布外电芯的平均绝对百分比误差达到21.8%。我们的方法强调了利用锂离子电池退化模式的领域知识指导特征工程的重要性。此外，我们向社区提供了一个新的公开电池老化数据集，其中电芯的循环使用已超过其额定容量的80%。