Fig. 6: Interpretations of the learned representations and ablation studies of the charging profile prediction model.

Box plots show the median (centre line), interquartile range (box limits), and whiskers (1.5 × the interquartile range). Results integrate data from both test sets. a t-distributed stochastic neighbour embedding (t-SNE) visualisation of the latent representations of charging data. Each point represents a full power/state-of-charge (SoC) charging profile, coloured according to its closest match with a reference profile obtained through charging under ideal laboratory conditions. b Zoomed-in views of the clusters formed in the t-SNE plot. The black curves, rendered with partial transparency, denote multiple charging profiles, and their overlap yields a grey appearance through cumulative opacity. c Performance of the temporal fusion transformer (TFT)-based charging profile prediction model compared against various baselines when evaluated on a combined dataset of test sets one and two. For TFT, three runs are shown directly as the lower error bar, bar height, and upper error bar; other models show a single run. d Performance of the charging profile prediction model trained with different configurations of static covariates, evaluated with normalised mean absolute error (MAE) in prediction. e Relative importance of the static covariates derived from the charging profile prediction model’s variable selection networks. The violin plots represent scaled kernel density estimates of relative feature importance, with overlaid box plots indicating medians and interquartile ranges for each static covariate. f Performance of the charging profile prediction model trained with three configurations of battery capacity: omitted, used as a continuous covariate, and used as a binned categorical covariate. For geographical generalisation, the capacity-omitted model is also evaluated on the Netherlands dataset, which lacks this feature.