Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

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.

Full text not available from this repository.
Official URL:


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.

Item Type: Article
Impact Factor: cited By 17
Uncontrolled Keywords: Artificial intelligence; Battery management systems; Biomimetics; Charging (batteries); Electric power transmission networks; Particle swarm optimization (PSO); Renewable energy resources; Swarm intelligence; Vehicle-to-grid, Accelerated particles; Gravitational search algorithm (GSA); Hybrid optimization; Meta heuristics; Optimization techniques; Plug in hybrid electric vehicles; State of charge; Use of renewable energies, Plug-in hybrid vehicles
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 03:05
Last Modified: 25 Mar 2022 03:05

Actions (login required)

View Item
View Item