Vasant, PandianPandianVasantBanik, AnirbanAnirbanBanikThomas, J. JoshuaJ. JoshuaThomasMarmolejo Saucedo, José AntonioJosé AntonioMarmolejo SaucedoGanesan, TimothyTimothyGanesanMunapo, EliasEliasMunapoManshahia, Mukhdeep SinghMukhdeep SinghManshahia2023-07-222023-07-222022https://scripta.up.edu.mx/handle/20.500.12552/421110.1016/B978-0-323-89785-3.00015-3Presently, 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.enAPSOGSAPlug-in hybrid electric vehiclesPSOPSOGSAState of ChargeSwarm intelligenceResource Types::text::book::book part