Fig. 3: Overview of the developed deep learning framework.
From: Deep learning predicts real-world electric vehicle direct current charging profiles and durations
a A high-level illustration of the AI-based system for direct current fast charging (DCFC). Charging session data are continuously collected from charging stations, aggregated on cloud servers, and delivered to high-performance computing (HPC) platforms for large-scale model training. The resulting trained models are deployed via cloud servers to provide real-time charging profile and time predictions, together with anomaly detection. b A schematic of the beta-variational autoencoder (β-VAE)-based anomaly detection model, which identifies irregularities in charging sessions by reconstructing the original power/state-of-charge (SoC) charging profile and evaluating the associated reconstruction error. RevIN denotes reversible instance normalisation, and long short-term memory (LSTM) is used for sequence modelling. c A schematic of the probabilistic charging profile prediction model, which predicts the quantiles of a full charging profile from partial inputs. Gated linear unit (GLU), gated residual network (GRN) and variable selection network (VSN), are key architectural components of the anomaly detection and charging profile prediction models, with additional details provided in Supplementary Fig. 1.
