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.

Batteries receive much media attention but they lag behind other technologies. These limitations become apparent when advancing the battery from portable uses to wheeled mobility that demands strenuous conformities and long service under harsh conditions.

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