Abstract：
Achieving precise estimates of battery cycle life is a formidable challenge due to the nonlinear nature of battery degradation. This study explores an approach using machine learning (ML) methods to predict the cycle life of lithium-metal-based rechargeable batteries with high mass loading LiNi0.8Mn0.1Co0.1O2 electrode, which exhibits more complicated and electrochemical profile during battery operating conditions than typically studied LiFePO₄/graphite based rechargeable batteries. Extracting diverse features from discharge, charge, and relaxation processes, the intricacies of cell behavior without relying on specific degradation mechanisms are navigated. The best-performing ML model, after feature selection, achieves an R2 of 0.89, showcasing the application of ML in accurately forecasting cycle life. Feature importance analysis unveils the logarithm of the minimum value of discharge capacity difference between 100 and 10 cycle (Log(|min(ΔDQ 100-10(V))|)) as the most important feature. Despite the inherent challenges, this model demonstrates a remarkable 6.6% test error on unseen data, underscoring its robustness and potential for transformative advancements in battery management systems. This study contributes to the successful application of ML in the realm of cycle life prediction for lithium-metal-based rechargeable batteries with practically high energy density design.
摘要：
由于电池退化的非线性特性，实现电池循环寿命的精确估计是一个巨大的挑战。本研究探索了一种使用机器学习（ML）方法来预测具有高质量负载LiNi0.8Mn0.1Co0.1O2电极的基于锂金属的可充电电池的循环寿命的方法，在电池运行条件下，其表现出比通常研究的基于LiFePO/石墨的可充电电池更复杂和电化学特征。从放电中提取不同的特征，charge,和放松过程，在不依赖于特定降解机制的情况下，细胞行为的复杂性被导航。性能最好的ML模型，特征选择后，R2为0.89，展示了ML在准确预测周期寿命中的应用。特征重要性分析揭示了100和10个循环之间放电容量差最小值的对数（Log（|min（ΔDQ100-10（V））|）作为最重要的特征。尽管固有的挑战，该模型在看不见的数据上显示出显着的6.6%的测试误差，强调其在电池管理系统中的鲁棒性和变革性进步的潜力。这项研究有助于ML在具有实际上高能量密度设计的基于锂金属的可充电电池的循环寿命预测领域的成功应用。
