Applied Materials Division Argonne National Laboratory Argonne IL 60439 United States
Chemical Sciences and Engineering Division Argonne National Laboratory Argonne IL 60439 United States
Data Science and Learning Division Argonne National Laboratory Argonne IL 60439 United States
Argonne Collaborative Center for Energy Storage Science (ACCESS) Argonne National Laboratory Argonne IL 60439 United States
准确的电池寿命估算有助于加快新型电池材料的设计，并确定最佳使用协议以延长部署寿命。不幸的是，传统的电池测试可能需要数年才能达到数千次循环。最近的研究表明，机器学习 (ML) 工具可以从 100 个或更少的初步循环（仅代表几周的循环）预测锂离子电池的寿命。到目前为止，关于这些预测在各种阴极化学中的有效性和广泛适用性的结论一直受到可用实验信息的限制。在这项工作中，我们利用代表六种正极化学物质（NMC111、NMC532、NMC622、NMC811、HE5050 和 5Vspinel）、多种电解质/负极成分的电池循环数据集，和 300 个精心准备的软包电池，以探索特征选择和电池化学在 ML 电池寿命预测中的作用。对于 100 个初步循环的化学跨越测试集，预测中的平均绝对误差 (MAE) 为 78 个循环。此外，仅使用第一个循环时，可以看到 103 个循环的 MAE。这项研究代表了对电池寿命预测的特征选择策略、ML 模型在多种电池化学中的泛化以及化学空间中训练集之外的预测的深入研究。仅使用第一个循环时，可以看到 103 个循环的 MAE。这项研究代表了对电池寿命预测的特征选择策略、ML 模型在多种电池化学中的泛化以及化学空间中训练集之外的预测的深入研究。仅使用第一个循环时，可以看到 103 个循环的 MAE。这项研究代表了对电池寿命预测的特征选择策略、ML 模型在多种电池化学中的泛化以及化学空间中训练集之外的预测的深入研究。
Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. A mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.
