An End-to-End Learning Framework for Early Prediction of Battery Capacity Trajectory

Accurately predicting the entire capacity trajectory using early-life data enables more efficient cell design, operation, maintenance, and evaluation for second-life use. To accomplish this challenging task, we propose an end-to-end learning framework combining empirical capacity fade models and data-driven machine learning models, in which the two types of models are closely coupled. First, we evaluate the accuracy of a library of relevant empirical models which have been shown to model the observed capacity fade of Li-ion cells with reasonable accuracy. After selecting a power-law model, we formulate an end-to-end learning problem that simultaneously fits the chosen powerlaw model to estimate the capacity fade curve and trains an elastic net to estimate the best-fit parameters of the empirical model. Our proposed end-to-end learning framework is evaluated using a publicly available battery dataset consisting of 124 lithium-iron-phosphate/graphite cells charged with various fast-charging protocols. This dataset was split into training, primary test, and secondary test datasets. Our method performs on par with existing early prediction methods in terms of cycle life prediction, attaining rootmean-square errors of 112 cycles and 165 cycles for primary and secondary test datasets, respectively. In addition to the cycle life prediction, our method possesses a unique ability to predict the entire capacity trajectory.