Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Abstract

Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the researches in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs. Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that include Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed, and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

Keywords

• apso
• electric vehicle
• gravitational search algorithm
• optimization
• pso
• psogsa
• swarm intelligence