Swarm Intelligence Based State-of-Charge Optimization for Charging Plug-in Hybrid Electric Vehicles
Transportation electrification has undergone major changes since the last decade. Success of the smart grid with renewable energy integration solely depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation sector. One of the key performance indicators in the hybrid electric vehicle is the State-of-Charge (SoC), which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged accelerated particle swarm optimization (APSO) technique was applied and compared with standard Particle swarm optimization (PSO), considering charging time and battery capacity. Simulation results obtained for maximizing the highly non-linear objective function indicate that APSO achieves some improvement in terms of best fitness and computation time.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://www.witpress.com/books/978-1-78466-095-6
-
Supplemental Notes:
- Abstract reprinted with permission from WIT Press.
-
Corporate Authors:
Ashurst Lodge
Ashurst, Southampton United Kingdom SO40 7AA -
Authors:
- Rahman, I
- Vasant, P M
- Singh, B S M
- Abdullah-Al-Wadud, M
-
Conference:
- 5th International Conference on Energy and Sustainability
- Location: Putrajaya , Malaysia
- Date: 2014-12-16 to 2014-12-18
- Publication Date: 2015
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 11p
- Monograph Title: Energy and Sustainability V: Special Contributions
-
Serial:
- WIT Transactions on Ecology and the Environment
- Volume: 206
- Publisher: WIT Press
- ISSN: 1743-3541
- Serial URL: http://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment
Subject/Index Terms
- TRT Terms: Battery chargers; Electric vehicle charging; Infrastructure; Optimization; Plug-in hybrid vehicles; Simulation; Stochastic processes; Sustainable development
- Candidate Terms: Smart grid
- Subject Areas: Energy; Environment; Vehicles and Equipment; I91: Vehicle Design and Safety;
Filing Info
- Accession Number: 01588469
- Record Type: Publication
- ISBN: 9781784660963
- Files: TRIS
- Created Date: Jan 28 2016 9:01AM