We find that upon acquisition of an EIS spectrum just before charging, both next cycle and longer term cell capacity can be predicted with a test error of less than 10%. We observe that our model is data-efficient, requiring just eight cells to attain a test error of less than 10%. Crucially, our approach is robust to dataset shift, attaining a test error of less than 7% on a dataset with a different distribution of cycling patterns to the training set. This is vital for deployment in the field where driving patterns may be different from those used to train the model. Finally, we demonstrate that, if available, using additional features based on historical capacity-voltage data can serve to augment the state representation and reduce average test error by up to 25%.