%0 Journal Article
%@ 11100168
%A Rahman, I.
%A Vasant, P.M.
%A Singh, B.S.M.
%A Abdullah-Al-Wadud, M.
%D 2016
%F scholars:7150
%I Elsevier B.V.
%J Alexandria Engineering Journal
%K Benchmarking; Charging time; Electric power transmission networks; Optimization; Particle accelerators; Particle swarm optimization (PSO); Smart power grids; Stochastic systems; Swarm intelligence; Vehicle performance, Accelerated particles; Key performance indicators; Nonlinear objective functions; PHEV; Plug-in hybrid electric vehicles; Renewable energy integrations; Smart grid; Transportation electrifications, Plug-in hybrid vehicles
%N 1
%P 419-426
%R 10.1016/j.aej.2015.11.002
%T On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles
%U https://khub.utp.edu.my/scholars/7150/
%V 55
%X Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs) for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC) which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged Accelerated particle swarm optimization (APSO) technique was applied and compared with standard particle swarm optimization (PSO) considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time. © 2015 Faculty of Engineering, Alexandria University.
%Z cited By 71