Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data

TL;DR

This paper develops a method to predict lithium-ion battery lifetime from early aging data using features derived from capacity-voltage data, achieving high accuracy even for out-of-distribution cells, and provides a new dataset for the community.

Contribution

It introduces a novel feature extraction approach from early-life capacity-voltage data and demonstrates its effectiveness in predicting battery lifetime across varying conditions.

Findings

15.1% mean absolute percentage error for in-distribution cells

21.8% error for out-of-distribution cells using hierarchical Bayesian regression

New publicly available battery aging dataset with cells cycled beyond 80% capacity

Abstract

Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.