Paper Title
AN AI BASED SCHEDULING ALGORITHM FOR EV CHARGING
Article Identifiers
Authors
M. Vasuki , Dr. T. Amalraj Victoire , D.Kishore
Keywords
Greedy algorithm , Electric vehicle (EV) charging, Time scheduling, Optimization, Peak demand reduction ,Cost minimization, Real-time scheduling, Computational resources, Battery levels, Predictive demand, Electricity prices, Implementation, Experimental results, Strengths ,Limitations, Future directions
Abstract
This paper investigates the application of the Greedy algorithm in electric vehicle (EV) charging time scheduling. The Greedy algorithm is a simple and intuitive approach that makes locally optimal decisions at each step without considering the global optimum. In the context of EV charging, it iteratively assigns charging time slots to vehicles based on immediate criteria such as current battery levels, predicted future demand, and electricity prices. This paper reviews the principles of the Greedy algorithm and explores its implementation in EV charging infrastructure. Experimental results demonstrate the effectiveness of the Greedy algorithm in optimizing charging schedules, reducing peak demand, and minimizing costs. Despite its simplicity, the Greedy algorithm offers practical solutions for real-time or near-real-time scheduling with limited computational resources. The paper concludes with discussions on the strengths, limitations, and future directions of the Greedy algorithm in EV charging time scheduling.
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How To Cite (APA)
M. Vasuki, Dr. T. Amalraj Victoire, & D.Kishore (May-2024). AN AI BASED SCHEDULING ALGORITHM FOR EV CHARGING . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), e329-e337. https://ijnrd.org/papers/IJNRD2405440.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : e329-e337
Other Publication Details
Paper Reg. ID: IJNRD_221681
Published Paper Id: IJNRD2405440
Downloads: 000121981
Research Area: Computer Science & TechnologyÂ
Country: villupuram, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405440.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405440
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