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Accurate battery lifetime prediction is important for preventativemaintenance, warranties, and improved cell design and manufacturing. However,manufacturing variability and usage-dependent degradation make life predictionchallenging. Here, we investigate new features derived from capacity-voltagedata in early life to predict the lifetime of cells cycled under widely varyingcharge rates, discharge rates, and depths of discharge. Features were extractedfrom regularly scheduled reference performance tests (i.e., low rate fullcycles) during cycling. The early-life features capture a cell's state ofhealth and the rate of change of component-level degradation modes, some ofwhich correlate strongly with cell lifetime. Using a newly generated datasetfrom 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide rangeof conditions, we demonstrate a lifetime prediction of in-distribution cellswith 15.1% mean absolute percentage error using no more than the first 15% ofdata, for most cells. Further testing using a hierarchical Bayesian regressionmodel shows improved performance on extrapolation, achieving 21.8% meanabsolute percentage error for out-of-distribution cells. Our approachhighlights the importance of using domain knowledge of lithium-ion batterydegradation modes to inform feature engineering. Further, we provide thecommunity with a new publicly available battery aging dataset with cells cycledbeyond 80% of their rated capacity.

Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data