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 th...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/11903/1/ESS14023FU1.pdf http://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/206/33184 http://eprints.utp.edu.my/11903/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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. |
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