These models also provide insights into the degradation of these cells. 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 thus, our work can serve as a comprehensive benchmarking study for this dataset. We first present the "capacity matrix" concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations.
In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. 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. 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.
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