  Accurate prediction of battery health is essential for real-world system
management and lab-based experiment design. However, building a life-prediction
model from different cycling conditions is still a challenge. Large lifetime
variability results from both cycling conditions and initial manufacturing
variability, and this -- along with the limited experimental resources usually
available for each cycling condition -- makes data-driven lifetime prediction
challenging. Here, a hierarchical Bayesian linear model is proposed for battery
life prediction, combining both individual cell features (reflecting
manufacturing variability) with population-wide features (reflecting the impact
of cycling conditions on the population average). The individual features were
collected from the first 100 cycles of data, which is around 5-10% of lifetime.
The model is able to predict end of life with a root mean square error of 3.2
days and mean absolute percentage error of 8.6%, measured through 5-fold
cross-validation, overperforming the baseline (non-hierarchical) model by
around 12-13%.
