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Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

Imran Rahman, Pandian Vasant, Balbir Singh Mahinder Singh, M. Abdullah-Al-Wadud
Copyright: © 2016 |Pages: 34
ISBN13: 9781466696440|ISBN10: 1466696443|EISBN13: 9781466696457
DOI: 10.4018/978-1-4666-9644-0.ch018
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MLA

Rahman, Imran, et al. "Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles." Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, et al., IGI Global, 2016, pp. 471-504. https://doi.org/10.4018/978-1-4666-9644-0.ch018

APA

Rahman, I., Vasant, P., Singh, B. S., & Abdullah-Al-Wadud, M. (2016). Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles. In P. Vasant, G. Weber, & V. Dieu (Eds.), Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics (pp. 471-504). IGI Global. https://doi.org/10.4018/978-1-4666-9644-0.ch018

Chicago

Rahman, Imran, et al. "Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, Gerhard-Wilhelm Weber, and Vo Ngoc Dieu, 471-504. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9644-0.ch018

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Abstract

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

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