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Accurate prediction of battery health is essential for real-world systemmanagement and lab-based experiment design. However, building a life-predictionmodel from different cycling conditions is still a challenge. Large lifetimevariability results from both cycling conditions and initial manufacturingvariability, and this -- along with the limited experimental resources usuallyavailable for each cycling condition -- makes data-driven lifetime predictionchallenging. Here, a hierarchical Bayesian linear model is proposed for batterylife prediction, combining both individual cell features (reflectingmanufacturing variability) with population-wide features (reflecting the impactof cycling conditions on the population average). The individual features werecollected 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 3.2days and mean absolute percentage error of 8.6%, measured through 5-foldcross-validation, overperforming the baseline (non-hierarchical) model byaround 12-13%.

Bayesian hierarchical modelling for battery lifetime early prediction