Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the "capacity matrix" concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset. These models also provide insights into the degradation of these cells. Our approaches can be used both to quickly train models for a new dataset and to benchmark the performance of more advanced machine learning methods.

翻译：电池使用寿命预测的数据驱动方法正在引起人们对退化机制不易理解和具备适当培训组合的应用的日益重视,然而,虽然先进的机器学习和深层次学习方法有望以极小的数据处理前处理达到高性能,但具有工程特点的更简单的线性模型往往能具有可比较性,特别是小型培训组,同时也提供物理和统计解释性。在这项工作中,我们使用以前公布的数据集来为电池使用寿命预测开发简单、准确和可解释的数据驱动模型。我们首先提出“能力矩阵”概念,作为电池电子化学循环数据以及一系列特征说明的缩略表。我们随后创建了一些单向和多变量模型,其中许多模型的性能与以前为该数据集公布的最优秀模型相当。这些模型还对这些细胞的退化情况提供了深刻的见解。我们的方法既可以用来快速培训新数据集模型,也可以用来衡量更先进的机器学习方法的性能。