Charging station is an important component for the healthy growth of the electric vehicle industry. Charging station refers to an infrastructure similar to petrol station (for conventional vehicle) that provides electric energy for the charging of plug-in hybrid electric vehicles (PHEVs). Many charging stations are on-street facilities provided by electric utility companies, mobile charging stations have been recently introduced. From the grid standpoint, a charging station is one way that the operator of an electrical power grid can adapt energy production to energy consumption, both of which can vary randomly over time. Basically, EVs in a charging station are charged during times when production exceeds consumption and are discharged at times when consumption exceeds production. In this way, electricity production need is not drastically scaled up and down to meet momentary consumption, which would increase efficiency and lower the cost of energy production and facilitate the use of intermittent energy sources, such as photovoltaic and wind.
Published in Chapter:
Swarm Intelligence-Based Optimization for PHEV Charging Stations
Imran Rahman (Universiti Teknologi Petronas, Malaysia),
Pandian Vasant (University of Technology Petronas, Malaysia), Balbir Singh Mahinder Singh (Universiti Teknologi Petronas, Malaysia), and M. Abdullah-Al-Wadud (King Saud University, Saudi Arabia)
Copyright: © 2015
|Pages: 32
DOI: 10.4018/978-1-4666-8291-7.ch012
Abstract
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 non-linear objective function evaluates the performance of both techniques in terms of global best fitness and computation time.