Accurate prediction of battery failure, both online and offline, facilitates design of safer battery systems through informed-engineering and on-line adaption to unfavorable scenarios. With the wide range of batteries available and frequently evolving pack designs, accurate prediction of cell behavior under different conditions is very challenging and extremely time consuming. In this issue of Joule, Li et al.¹ used data from a previously reported finite-element model to train machine learning algorithms to predict whether a cell will undergo an internal short circuit when exposed to a selection of mechanical abuse conditions. The presented approach aims to alleviate, and yet is still limited by, a common challenge facing data-driven prediction methods: access to robust, plentiful, high-quality, and relevant experimental data.

无论是在线还是离线，准确的电池故障预测都可以通过明智的工程设计和在线适应不利情况来促进设计更安全的电池系统。随着可用电池的种类繁多以及电池组设计不断发展，准确预测不同条件下的电池性能非常具有挑战性，并且非常耗时。在本期《焦耳》中，Li等人。^1个使用先前报告的有限元模型中的数据来训练机器学习算法，以预测当暴露于各种机械滥用条件下时电池是否会发生内部短路。提出的方法旨在缓解但仍然受到数据驱动的预测方法面临的一个共同挑战：访问可靠，丰富，高质量和相关的实验数据。