Bayesian hierarchical modelling for battery lifetime early prediction
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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 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 cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13
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