Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …

The majority of global road transportation emissions come from passenger and freight
vehicles. Electric vehicles (EV) provide a sustainable transportation way, but customers' …

Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …

Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to
the operation of EV fleets and charging stations. This paper develops a Long Short-Term …

This paper performs a novel data-driven approach to optimize electric vehicle (EV) public
charging. We translate the study area into a directed graph by partitioning it into discrete …

As the market penetration of electric vehicles (EVs) increases, the surge of charging demand
could potentially overload the power grid and disrupt infrastructure planning. Hence, an …

As an effective technology to reduce traffic pollution emissions, the new energy vehicle
industry has developed rapidly in recent years, and the sales of new energy vehicles have …

The large fluctuations in charging loads of electric vehicles (EVs) make short-term
forecasting challenging. In order to improve the short-term load forecasting performance of …

Electric vehicles (EVs) are predicted to be highly integrated into future smart grids
considering their significant role in achieving a safe environment and sustainable …

As climate change has become a pressing concern, promoting electric vehicles'(EVs) usage
has emerged as a popular response to the pollution caused by fossil‐fuel automobiles …