With the fast development of electrifications of vehicles, EV charging stations are booming in
coming years. Meanwhile, the growing demand for charging power, and the stochastic …

Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

In this paper, we propose novel approaches using state-of-the-art machine learning
techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These …

Although AI-empowered schemes bring some sound solutions to stimulate more reasonable
energy distribution schemes between charging stations (CSs) and CS providers, frequent …

Internal and external circumstances affect a vehicles' fuel consumption. Various studies
reviewed internal combustion engine vehicles (ICEVs) energy consumption. While Battery …

In this paper, we present an ensemble stacked generalization (ESG) approach for better
prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of …

At present, the new energy vehicle (NEV) industry in China is at a huge risk of overheated
investment and overcapacity. An accurate prediction of China's future NEV market is of great …

Among the many approaches towards fuel economy, the adoption of electric vehicles (EV)
may have the greatest impact. However, existing studies on EV adoption predict very …

In this paper, we propose a novel economic-efficiency framework for an electric vehicle (EV)
network to maximize the profits (ie, the amount of money that can be earned) for charging …

The immense growth and penetration of electric vehicles has become a major component of
smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the …