Lithium-ion batteries are ubiquitous in applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this Article, we design and evaluate a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root-mean-squared error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and could be applied to other critical components that require real-time estimation of SOH.

锂离子电池在从便携式电子产品到电动汽车的各种应用中无处不在。无论应用如何，车载计算机对电池健康状态 (SOH) 的可靠实时估计对于电池的安全运行至关重要，最终保障资产的完整性。在本文中，我们设计和评估了一个机器学习管道，用于估计在各种条件下循环的 179 个电池的电池容量衰减（一种电池健康指标）。该管道通过使用两个参数和两个非参数算法来估计具有相关置信区间的电池 SOH。使用充电电压和电流曲线段，管道工程师 30 特征化、执行自动特征选择和校准算法。当部署在快速充电协议下运行的电池上时，最佳模型的均方根误差为 0.45%。这项工作为电池 SOH 估计的可扩展数据驱动模型的设计提供了见解，强调了围绕预测的置信区间的价值。管道方法将实验数据与机器学习建模相结合，可应用于需要实时估计 SOH 的其他关键组件。