Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles
Journal
Advances of Artificial Intelligence in a Green Energy Environment
Date Issued
2022
Author(s)
Vasant, Pandian
Banik, Anirban
Thomas, J. Joshua
Marmolejo Saucedo, José Antonio
Ganesan, Timothy
Munapo, Elias
Manshahia, Mukhdeep Singh
Type
text::book::book part
Abstract
Presently, there is a significant emphasis on green technology in order to increase the usage of clean energy sources in the transportation sector while also reducing emissions. At this phase, a sufficient charge allocation strategy is needed to use plug-in hybrid power vehicles (PHEVs), including the implementation of smart charging infrastructure and intelligent grid networks. Daytime charging stations are needed for PHEV regular use, and at this stage, only adequate charging control and infrastructure management will contribute to broader PHEV adoption. The researchers are attempting to establish an effective control system for filling as well as promoting the penetration of upcoming PHEVs on highways. In this case, intelligent energy management necessitates the creation of statistical models over optimization strategy focused on computer intelligence. The state of charge of PHEVs was optimized employing particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization (APSO), and a combined form of PSO and GSA (PSOGSA). In this perspective, the individual and comparative performance of four techniques was defined in terms of convergence speed, computation time, and best fitness. © 2022 Elsevier Inc. All rights reserved.