According to disruptive innovation theory, customers are swayed by new market entrants through low-end encroachment strategies and/or new market disruption, but these predictions do not account for the rising interest in product categories such as electric vehicles in emerging markets.

Fuel cell hybrid electric vehicles (FCHEVs) are emerging as a promising alternative for a more environment-friendly transportation. This article recommends an energy management system (EMS) for FCHEVs. The EMS is based on nonlinear model predictive control (NMPC) and employs a recurrent neural network (RNN) for modeling a proton exchange membrane FC.

This article proposes an EVWCS using interleaved boost converter (IBC) and flat solenoid coupler (FSC). The closed-loop control between the primary and secondary is realized by adjusting the duty cycle of the IBC.

Electric vehicle (EV) technology is still developing, and most of the available brands market them as either environmentally friendly or operationally economical compared to internal combustion engine based traditional vehicles.

In this paper the authors have proposed a contract between an aggregator and a fleet of electric vehicles, allowing to design prices that induce users to modify their energy consumption in demand response programs. A demand response program for electric vehicles (EV) is proposed to control the charging decision process in EV clusters.

The aim of this work is to design a charging power schedule for each unit able to minimize the overall daily peak power while satisfying the customer requirements in terms of charged energy.

This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy.

This article tries to develop a new analytical reliability and adequacy model of smart grids, considering the precise model of PHEVs.

In this paper, the authors establish a double-stage coordinative decision-making (DCD) framework for the BSCS configuration, using the distributed robust optimization (DRO) approach for multi-timescale battery inventories.

Vehicle velocity is a challenging research because vehicle velocity is influenced by various factors such as driving style, driving pattern, traffic condition. An accurate vehicle velocity prediction is of great significance to energy consumption research of electric vehicle.