Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

Rahman, Imran and Vasant, Pandian and Mahinder Singh, Balbir Singh and Abdullah-Al-Wadud, M. (2015) Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles. [Citation Index Journal]

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Official URL: http://dx.doi.org/10.1155/2015/620425


Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

Item Type:Citation Index Journal
Impact Factor:0.762
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Academic Subject One:Mission Oriented Research - Green Technologies - Water - Natural gas - Optimization
Academic Subject Two:Academic Department - Electrical And Electronics - Instrumentation and Control - Robotics - Hybrid vehicle
Departments / MOR / COE:Departments > Fundamental & Applied Sciences
ID Code:11896
Deposited By: Pandian Vasant
Deposited On:07 Oct 2016 01:42
Last Modified:07 Oct 2016 01:42

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