Hybrid Swarm Intelligence-Based Optimization for Charging Plug-in Hybrid Electric Vehicle

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• 作者：Imran Rahman (7)
  Pandian Vasant (7)
  Balbir Singh Mahinder Singh (7)
  M. Abdullah-Al-Wadud (8)
  
  7. Department of Fundamental and Applied Sciences ; Universiti Teknologi PETRONAS ; Tronoh ; Malaysia
  8. Department of Software Engineering ; College of Computer and Information Sciences ; King Saud University ; Riyadh ; KSA
  
• 关键词：Smart charging ; State ; of ; charge ; Plug ; in hybrid electric vehicle ; PSOGSA ; Swarm intelligence
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9012
• 期：1
• 页码：22-30
• 全文大小：240 KB
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• 作者单位：Intelligent Information and Database Systems
• 丛书名：978-3-319-15704-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Plug-in hybrid electric vehicle (PHEV) has the potential to facilitate the energy and environmental aspects of personal transportation, but face a hurdle of access to charging system. The charging infrastructure has its own complexities when it is compared with petrol stations because of the involvement of the different charging alternatives. As a result, the topic related to optimization of Plug-in hybrid electric vehicle charging infrastructure has attracted the attention of researchers from different communities in the past few years. Recently introduced smart grid technology has brought new challenges and opportunities for the development of electric vehicle charging facilities. This paper presents Hybrid particle swarm optimization Gravitational Search Algorithm (PSOGSA)-based approach for state-of-charge (SoC) maximization of plug-in hybrid electric vehicles hence optimize the overall smart charging.