Statistical learning for accurate and interpretable battery lifetime prediction

TL;DR

This paper develops simple, interpretable models for battery lifetime prediction using a novel capacity matrix representation, achieving performance comparable to complex methods and providing insights into degradation mechanisms.

Contribution

It introduces the capacity matrix concept and demonstrates that simple models can match advanced methods in accuracy while offering interpretability.

Findings

Simple models achieve comparable accuracy to complex models.

Capacity matrix effectively summarizes electrochemical cycling data.

Models provide insights into battery degradation processes.

Abstract

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.