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

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• 作者：Imran Rahman^a ; ^{imran.iutoic@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Pandian M. Vasant^a ; ^{pvasant@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Balbir Singh Mahinder Singh^a ; ^{balbir@petronas.com.my" class="auth_mail" title="E-mail the corresponding author} ; M. Abdullah-Al-Wadud^b ; ^{mwadud@ksu.edu.sa" class="auth_mail" title="E-mail the corresponding author}
• 关键词：PHEV ; Optimization ; Swarm intelligence ; Smart grid ; Particle swarm optimization ; Accelerated particle swarm optimization
• 刊名：Alexandria Engineering Journal
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：55
• 期：1
• 页码：419-426
• 全文大小：726 K

文摘

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.