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

Rahman, I. and Vasant, P.M. and Singh, B.S.M. and Abdullah-Al-Wadud, M. (2016) Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review. Intelligent Decision Technologies, 10 (2). pp. 149-163. ISSN 18724981

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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-inhybrid 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. © 2016 IOS Press and the authors. All rights reserved.

Item Type: Article
Additional Information: cited By 13
Uncontrolled Keywords: Artificial intelligence; Carbon; Charging (batteries); Electric power transmission networks; Hybrid vehicles; Optimization; Plug-in electric vehicles; Smart power grids; Vehicles; Wind power, Literature reviews; Metaheuristic; PHEV; Smart grid; Swarm Intelligence, Plug-in hybrid vehicles
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:18
Last Modified: 09 Nov 2023 16:18
URI: https://khub.utp.edu.my/scholars/id/eprint/7147

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