Affiliations: [a] Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia | [b] Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, KSA
Correspondence:
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Corresponding author: Imran Rahman, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia. E-mail:imran.iutoic@gmail.com
Abstract: 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.
Keywords: PHEV, metaheuristic, optimization, swarm intelligence, literature review, smart grid
