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

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 alloc...

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Main Authors: Rahman, I., Vasant, P.M., Mahinder Singh, B.S., Abdullah-Al-Wadud, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.26088 /
Published: Hindawi Limited 2015
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937780741&doi=10.1155%2f2015%2f620425&partnerID=40&md5=f161d8c6fb2ec2407020c3e589acc46d
http://eprints.utp.edu.my/26088/
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spelling utp-eprints.260882021-08-30T08:51:42Z Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles Rahman, I. Vasant, P.M. Mahinder Singh, B.S. Abdullah-Al-Wadud, M. 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. © 2015 Imran Rahman et al. Hindawi Limited 2015 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937780741&doi=10.1155%2f2015%2f620425&partnerID=40&md5=f161d8c6fb2ec2407020c3e589acc46d Rahman, I. and Vasant, P.M. and Mahinder Singh, B.S. and Abdullah-Al-Wadud, M. (2015) Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles. Mathematical Problems in Engineering, 2015 . http://eprints.utp.edu.my/26088/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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. © 2015 Imran Rahman et al.
format Article
author Rahman, I.
Vasant, P.M.
Mahinder Singh, B.S.
Abdullah-Al-Wadud, M.
spellingShingle Rahman, I.
Vasant, P.M.
Mahinder Singh, B.S.
Abdullah-Al-Wadud, M.
Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
author_sort Rahman, I.
title Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
title_short Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
title_full Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
title_fullStr Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
title_full_unstemmed Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
title_sort swarm intelligence-based smart energy allocation strategy for charging stations of plug-in hybrid electric vehicles
publisher Hindawi Limited
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937780741&doi=10.1155%2f2015%2f620425&partnerID=40&md5=f161d8c6fb2ec2407020c3e589acc46d
http://eprints.utp.edu.my/26088/
_version_ 1741197081444352000
score 11.62408

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