None of the studies discussed above have published their datasets; therefore, it is not possible to conduct comparative evaluations or to validate claims. In this work we share an experimental dataset containing acoustic signals from 7 cells cycled using a multi-C-rate protocol. We demonstrate visually that acoustic signals from different cells are heterogeneous when simple features are considered, and proceed to explore whether different feature configurations can result in greater levels of homogeneity across cells and lead to state estimators that can successfully generalise. State estimators are constructed in the form of feedforward neural networks (FNNs) and convolutional neural networks (CNNs), whose input is a certain feature configuration and whose output is the cell voltage. This is a regression task. For each feature configuration, estimators with a range of fitting capabilities are trained and tested.