The above studies advocate for their respective feature extraction technique based on criteria such as stability, monotonicity or linearity of the correlations to the SoC. Such characteristics have been shown to be present in any one cell; nevertheless, they have not been shown to be consistent in a population so as to allow a generalised SoC estimator to be fitted for a specific battery product (i.e. for a catalogue-listed cell). In cases where SoC models were in fact fitted, the underlying dataset typically consisted of signals obtained at a single location of a single cell . This monolithic nature of the datasets has resulted in a variety of modelling techniques being successful in estimating the SoC of a single cell, and various types of artificial neural networks have performed particularly well . Nevertheless, in the absence of a benchmark test, different methods cannot be compared and the accuracies reported by different studies are not worth noting. Furthermore, single-location testing is prone to producing datasets that correlate acoustic waveforms to the local SoC of the tested region, instead of the global SoC of the whole cell. Spatial variations of the SoC, especially in large-format cells, have been demonstrated by several diffraction studies .