On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles

Alexandria Engineering Journal

Pandian M. Vasant¹  Balbir Singh Mahinder Singh¹  Imran Rahman¹  M. Abdullah-Al-Wadud² 

[1] Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia;Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;

关键词: PHEV;    Optimization;    Swarm intelligence;    Smart grid;    Particle swarm optimization;    Accelerated particle swarm optimization;   

DOI  :  10.1016/j.aej.2015.11.002

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown