Swarm Intelligence-Based Optimization for PHEV Charging Stations
In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging ti...
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| Main Authors: | , , , |
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| Format: | Book Section |
| Published: |
IGI Global
2015
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/11901/1/Swarm-Intelligence-Based-Optimization-for-PHEV-Charging-Stations.pdf http://www.igi-global.com/book/handbook-research-swarm-intelligence-engineering/121216 http://eprints.utp.edu.my/11901/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly nonlinear objective function evaluates the performance of both techniques in terms of global best fitness and computation time. |
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