relation: https://khub.utp.edu.my/scholars/12717/
title: Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle
creator: Vasant, P.
creator: Marmolejo, J.A.
creator: Litvinchev, I.
creator: Aguilar, R.R.
description: Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
publisher: Springer
date: 2020
type: Article
type: PeerReviewed
identifier:   Vasant, P. and Marmolejo, J.A. and Litvinchev, I. and Aguilar, R.R.  (2020) Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle.  Wireless Networks, 26 (7).  pp. 4753-4766.  ISSN 10220038     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066101178&doi=10.1007%2fs11276-019-01993-w&partnerID=40&md5=471dbdf820260cad982edaa31169233a
relation: 10.1007/s11276-019-01993-w
identifier: 10.1007/s11276-019-01993-w