Enhancing Electric Vehicle Range Prediction through Deep Learning: An Autoencoder and Neural Network Approach - Airlangga, Gregorius (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2024)
The burgeoning adoption of electric vehicles (EVs) signifies a pivotal shift towards sustainable transportation, necessitated by the global imperative to mitigate climate change impacts. Central to this transition is the resolution of range anxiety, a significant barrier impeding wider EV acceptance. This research introduces a novel deep learning framework combining autoencoders and deep neural networks (DNNs) to predict EV range more accurately and reliably. Leveraging a comprehensive dataset from the