Lastly, it is worth noting that state estimation on multiple cells using a common model was shown to be possible. MAE close to 15 mV in the voltage estimation task was demonstrated on the seven-cell dataset, and could be further improved by allowing models to train for longer or by fine-tuning their structure and hyperparameters. However, it is critical to recognise that this represents a type of overfitting, where the training process has led to the identification of cell-specific patterns between the acoustic waveforms and the cell state. The implication is that a battery management system using acoustics for state estimation would require every cell to undergo prior testing and to contribute some data for model training. A complementary perspective is to think that the data generating distribution is strongly multimodal, having a different mode per cell. This also explains the ease of the classification task, and the strong presence of clustering-by-cell in reduced dimensions which emerge without supervision.