Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. Data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.

准确预测电池故障将使电池系统更安全，并加快电池设计和制造过程，以提高一致性和可靠性。数据驱动的预测方法已显示出以较低的计算成本准确预测细胞行为的前景，但训练起来却很昂贵。此外，由于电池故障的风险已经非常低，因此收集足够的相关数据以促进数据驱动的预测非常具有挑战性。在这里，概述了设计实验以促进相对较少数量的测试，处理数据，应用数据驱动的方法以及增进我们对行为指示物理学的理解的观点。