The transportation sector accounts for a significant share of greenhouse gas emissions.
Hence, the electrification of this sector is a crucial contributor to the mitigation of global …

In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

The healthcare supply chain involves the manufacturing and delivery of medicines at the
right time, at the right place, and in the correct quantity. In the world of uncertainties …

The spatial–temporal distribution of electric vehicle (EV) charging load has strong
randomness and is affected by battery capacity and user behavior. In addition, the multinode …

In recent years, many countries have set specific goals to replace fossil fuel vehicles with the
electric ones due to environmental concerns and issues related to energy supply security; it …

Electric vehicles (EVs) are the most important components of smart transportation systems.
Limited driving range, prolonged charging times, and inadequate charging infrastructure are …

The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are
becoming increasingly popular for their contribution in reducing greenhouse gas emissions …

Transportation electrification is a valid option for supporting decarbonization efforts but, at
the same time, the growing number of electric vehicles will produce new and unpredictable …

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 …