  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.
