Results 51 to 60 of about 1,731 (188)

Optimizing Electric Vehicle Charging Scheduling With Deep Q Networks and Long Short‐Term Memory‐Based Electricity and Battery State of Charge Prediction

Energy Science &Engineering, Volume 14, Issue 5, Page 2583-2599, May 2026.
Schematic diagram showing the proposed approach for EV charging/discharging. ABSTRACT The number of electric vehicles (EVs) on the road is rising as a result of recent advancements in EV technology, and EVs are important to the smart grid economy. Demand response schemes involving electric vehicles have the potential to dramatically reduce the cost of ...
F. Zonuntluanga, Subhasish Deb, Kshetrimayum Robert Singh, Subir Datta, Ahmet Onen, Taha Selim Ustun, Jaesung Jung   +6 more
wiley   +1 more source

V2G Scheduling Control Strategy Based on Virtual Synchronization

南方能源建设
[Objective] With the rapid increase in the number of electric vehicles (EVs), the impact of EV energy storage on the power grid has become increasingly significant.
Wei ZHENG, Le ZHANG, Jianjun ZHANG, Changling WU, Haoran ZHAO, Kuo YAN   +5 more
doaj   +1 more source

POWER: Performance Optimization With Evaluated Results for HEV Battery Selection via MCDM‐TOPSIS

Energy Science &Engineering, Volume 14, Issue 4, Page 2103-2117, April 2026.
ABSTRACT The increasing transportation demands and environmental concerns in India necessitate the selection of optimal battery technologies for hybrid electric vehicles (HEVs). As the fifth‐largest car market globally, India faces rising vehicle demand, while the transportation sector remains a major contributor to air pollution.
Rinku Kumar Roy, Harpreet Kaur Channi, Sita Rani, Aman Kataria, Parveen Bajaj, George Uwadiegwu Alaneme   +5 more
wiley   +1 more source

A dispatch capacity assessment method for electric vehicle clusters considering user willingness

Zhejiang dianli
To address how variable user participation willingness affects electric vehicle （EV） dispatchable capacity assessment， this paper proposes a novel assessment method incorporating user willingness.
LI Zhenkun, HU Kun, YAO Yicong
doaj   +1 more source

Understanding the Autonomous Electric Vehicle Cyber Threat Landscape: A Focus on Infrastructure, Threats and Ontology‐Based Modelling

Energy Internet, Volume 3, Issue 1, Page 39-51, April 2026.
ABSTRACT The development of autonomous electric vehicles (AEVs) represents the convergence of two simultaneous automotive revolutions: electric vehicles (EVs) and autonomous vehicles (AVs). AVs require sensors, decision‐making systems and actuation systems to achieve autonomous driving, whereas EVs require intelligent management and real‐time ...
Ohud Alsadi, Abdulrahman Almijlad, Trinadh Pamulapati, Rajiv Ranjan, Devki Jha, Tejal Shah   +5 more
wiley   +1 more source

Elektrikli Araç Şarj İstasyonlarında Güç Elektroniği Topolojileri: Klasik Yapılardan Gelişmiş Çözümlere

Gazi Üniversitesi Fen Bilimleri Dergisi
Elektrikli araçların (EV) yaygınlaşmasıyla birlikte, yüksek verimli, güvenli ve hızlı şarj altyapılarına olan ihtiyaç artmaktadır. Bu durum, güç elektroniği tabanlı dönüştürücü topolojilerinin performans, maliyet, kompaktlık ve enerji yönetimi açısından ...
Uğur Fesli
doaj   +1 more source

Modelling Charging Infrastructure in V2G Scenario

SAE International Journal of Advances and Current Practices in Mobility
<div class="section abstract"><div class="htmlview paragraph">Nowadays, electrification is largely acknowledged as a crucial strategy to mitigate climate change, especially for the transportation sector through the transition from conventional vehicles to electric vehicles (EVs). As the demand for EVs continues to rise, the development of a
Innocenti, Eleonora, Berzi, Lorenzo, Kociu, Aljon, Delogu, Massimo   +3 more
openaire   +1 more source

Simulation and Modeling Techniques for Multi‐Objective Optimization of Hybrid Fast EV Charging Station

Battery Energy, Volume 5, Issue 2, March 2026.
Monte Carlo simulation (MCS) provides a probabilistic view of daily electric vehicle (EV) charging demand. The simulation model helps predict charging demand patterns in the absence of real‐time data on Electric Vehicles (EVs) in a particular location. It uses a bimodal Gaussian distribution to simulate morning and evening arrival peaks of the EVs [30],
Sana Sultan, Fawad Azeem, Habib Ullah Manzoor, Ghous Bakhsh Narejo, Tareq Manzoor   +4 more
wiley   +1 more source

V2G Transfer of Energy to Various Applications [PDF]

E3S Web of Conferences, 2019
In today’s world, there is a need of verge of significantant transformation in Electrical Power System. The Vehicle-to-Gird (V2G) concept optimizes this transformation. The PEV typically has a higher capacity Energy Storage System (ESS).
Sree lakshmi Dr. G., Divya G., Sravani G.   +2 more
doaj   +1 more source

An improved network simulator for EV / V2G studies [PDF]

CIRED Workshop 2016, 2016
An 'Improved Network Simulator' (INSim) is described, offering EV and V2G aggregation on realistic networks. INSim models distributed loads and supplies including V2G and various ESS on an arbitrary network solved by EPRI's OpenDSS. INSim is a work in progress and is to be accessible via an Open Source platform.
Broderick, Stephen, Cruden, Andrew, Sharkh, Suleiman, Bessant, Nigel   +3 more
openaire   +1 more source