In this study, the parameters affecting the transistor and diode junction temperatures of the battery group, which is the most important component of a battery electric vehicle, have been extensively analyzed and estimated by the machine learning approach.

This article contributes to energy planning through the long-term projection of the Brazilian fleet of light vehicles, and the simulation of their impacts on energy demand, in three scenarios of insertion of plug-in electric vehicles.

In this work, the authors investigated the problem of scheduling the charging of electric vehicles to reduce the maximum peak power while satisfying all charging demands.

This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast.

To evaluate the effectiveness of the proposed control architecture, extensive simulation analysis was carried out using the MiTraS virtual testing platform, considering two realistic driving scenarios.

In this paper, the authors developed a DP-based heuristic, which combined Restricted DP and Prim’s algorithms, to solve larger instances.

Based on the Stackelberg Game Model, this research develops an ideal operation model for electric vehicles in electric power aggregators (EPAs).

This paper shows a comprehensive review of power management system (PMS) in fuel cell electric vehicles.

In this paper, a vehicle thermal management control approach based on dynamic programming was created in order to better access the energy-saving potential of the thermal management system of pure electric vehicles and to increase the driving range in low-temperature environments.

This review provides a synthesis and critical discussion of the most up-to-date evidence on public charging choices based on which a conceptual framework to match choices and their determinants is devised.