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 research 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 includes 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.

An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation

“…The description and parameter settings for APSO and its dominance over PSO for multi‐objective problems is explained in, the typical values for this accelerated PSO are and ; however, and are recommended . Size of swarm 100; Maximum no.…”

A heuristically optimized comprehensive charging scheme for large‐scale EV integration

“…The description and parameter settings for APSO and its dominance over PSO for multi‐objective problems are explained in Vasant et al; the typical values for this accelerated PSO are α ≈ 0.1~0.4 and β ≈ 0.1~0.7; however, α ≈ 0.2 and β ≈ 0.5 are recommended in Vasant et al Size of swarm 100; maximum no. of steps 100; α = 0.2; β = 0.5; maximum iteration 200; no.…”

A comprehensive review on theoretical framework-based electric vehicle consumer adoption research

“…A PHEV works on electrical power or ICE, but usually has a little range using only electricity. The driving range powered by electrical energy is typically lengthier in BEVs than in PHEVs, because BEVs are only run by electricity [31]. A BEV consists of the electric drivetrain alone.…”

“…This system is power-driven from a bulk volume battery (compared to E-REV and PHEV) that is re-energised from a power grid [30]. The driving range powered by electrical energy is typically lengthier in BEVs than in PHEVs, because BEVs are only run by electricity [31]. The author in [26] claimed that although HEVs were regarded to be EVs in some past literatures, they are mostly fuel-efficient vehicles that do not need a radical performance shift by consumers.…”