Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review

• Main Authors: Rahman, Imran, Vasant, Pandian, Mahinder Singh, Balbir Singh, Abdullah-Al-Wadud, M.
• Format: Citation Index Journal
• Published: 2016
• Subjects: QA Mathematics; QA75 Electronic computers. Computer science; TK Electrical engineering. Electronics Nuclear engineering; TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://eprints.utp.edu.my/11905/1/idt-10-idt245.pdf; http://eprints.utp.edu.my/11905/

Hybrid Vehicles have experienced major modifications since the last decade. Smart grid success with combination of renewable energy exclusively depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation. Recent technical studies regarding various optimization strategies related to PHEV integrated smart grid; such as control and battery charging, vehicle-to-grid (V2G), unit commitment, charging infrastructures, integration of solar and wind energy and demand management prove that electrification of transportation as a rapidly growing field of research. This work presents a holistic review of all substantial research applying metaheuritics optimization for plug-in hybrid electric vehicles. A summary on future perspective of metaheuristic algorithms is also provided, covering Cuckoo Search(CS), Harmony Search (HS), Artificial Bee Colony (ABC), etc. with a comprehensive reviews on previously applied methods and their performance for solving different real-world problems in the domain of PHEVs. Moreover, significant shifts towards hybrid and hyper metaheuristics are also highlighted.