Huang et al. deviated from the single-cell single-location paradigm in two ways. Firstly, they trained an SoC estimator using waveforms from 150 locations of one cell and 42 SoCs (that amounts to 6300 waveforms). Secondly, as an assessment of generalisation they used their trained estimator to infer the SoC of two other cells, using signals from 6 different locations on each. A point of novelty is that they did not compare their predictions to the aggregate SoC of those cells, but to the local SoC of those specific points, measured destructively by inductively coupled plasma-induced optical emission spectroscopy (ICP-OES). Although this generalisation test cannot be considered extensive, due to the small number of spectroscopic test points, it would not be practical to perform ICP-OES much more widely. A complementary assessment could have evaluated the trained model on more locations of the test cells, comparing predictions to the cell-level SoC. The authors had 141,750 such datapoints from each cell in their disposal (obtained from 3,375 locations and 42 SoCs), and additional model validation using this dataset would be useful in the future. also deviated from the singlecell paradigm by training a Support Vector Machine with acoustic data from 2 cells and predicting the SoC of a third cell, although the size of the dataset and the number of training and test samples were not specified in their study.