Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles

Khush Bakht, Syed Abdul Rahman Kashif, Muhammad Salman Fakhar, Irfan Ahmad Khan () and Ghulam Abbas ()
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Khush Bakht: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Syed Abdul Rahman Kashif: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Muhammad Salman Fakhar: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Irfan Ahmad Khan: Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USA
Ghulam Abbas: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan

Energies, 2023, vol. 16, issue 7, 1-23

Abstract: Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms.

Keywords: all-electric vehicles (AEVs); analysis of variance (ANOVA); accelerated particle swarm optimization (APSO); battery; hybrid electric vehicles (HEVs); plugin electric vehicles (PEVs); plugin hybrid electric vehicles (PHEVs); state of charge (SoC); utility power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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