Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle
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Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques.
KeywordsNature-inspire metaheuristics Hybrid optimization Swarm intelligence Artificial intelligence State-of-charge optimization Plug-in hybrid electric vehicle
The authors would like to sincerely thank Mr. Imran Rahman, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Malaysia for his great help and support in this research work. This research project also supported by Modeling Evolutionary Algorithms Simulation and Artificial Intelligence (MERLIN), Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Faculty of Science and Information Technology, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.
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Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
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