A common pitfall of machine learning applied to battery studies has been demonstrated using an acoustic dataset. It was shown that models with a large number of parameters can have a high enough capacity to capture cell-specific patterns implicitly, without discovering any connecting patterns between cells. This represents a type of overfitting that is frequently disregarded in the literature, and a lack of generalisation which presents a challenge to the wider use of acoustics for battery state estimation. The dataset is the first of its kind to be made available, alongside the trained models , and the shared code can be used to reproduce the analysis .