Finally, we compare "charge" and "discharge" models when deployed on a subset of cells from the dataset that were cycled identically. This special case examines how we can predict the lifetime of cells in which variation arises purely from intrinsic differences between cells, with one use case being production quality control. We train and test these two models separately with features from cycles 141-150, and then deploy them on a test set of 6 unseen cells cycled identically. As seen in Table , the "discharge" model outperforms the "charge" model. This trend is opposite to the model trained on the full dataset, which includes cells from many charging conditions. Thus, "Class 1" features are less capable of capturing intrinsic cell-to-cell variability than "Class 2" features, because the predictive power of the former relies strongly on the charging protocol encoded in the data (which is not varied in this subset of the data). One limitation of the Severson dataset is that it does not contain many repeats of the same charging protocol. As a result, as discussed in the SI, the statistical relevance of the transferability study is limited (Dechent et al. showed that at least 9 repeats are needed to capture the underlying variability of a cell population). This emphasizes the need to design specific datasets tailored for specific applications. The analysis and results are reproducible using different sets of features.