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Smart Charging for Electric Vehicles: A Meta-Heuristic Analysis

Published: 13 May 2024 Publication History

Abstract

Because of their revolutionary technology and positive impact on the environment, electric vehicles (EVs) have quickly become acknowledged as game-changers in the car industry. This paper presents a thorough analysis and review of optimizing electric vehicle charge scheduling using meta-heuristic optimization techniques. It begins with a basic introduction to electric vehicle charging systems, then explains why optimization is essential and the difficulties that come with integrating a large number of EVs into the power grid. Concerns about grid stability, power quality interruptions, and increased expenses have been raised in response to the increasing number of electric vehicles on the road. A meta-heuristic optimization method's ability to find near-optimal solutions in complex search landscapes makes it a powerful tool for electric vehicle charge scheduling, which is crucial for addressing these difficulties. In this paper, we provide a comprehensive analysis of many meta-heuristic methods, including Genetic Algorithms, Ant Colony Optimization, Simulated Annealing, and Particle Swarm Optimization. We highlight their approaches, strengths, and weaknesses as they pertain to the scheduling of electric vehicle charges. The research suggests that while there are benefits to using any given approach, the limitations and goals of a given challenge typically dictate which strategy is most appropriate. While some methods perform better in real-time settings, others are well-suited to large-scale improvements that don't need any network connectivity. In addition, the study emphasizes the possibility of hybrid approaches that combine elements of several meta-heuristics to improve the optimization procedure. In conclusion, the paper has demonstrated the importance of meta-heuristic optimization strategies for efficient EV charging scheduling. Strategically utilizing these strategies is considered essential for the sustainable integration of electric vehicles into modern power networks, given the expected continuous expansion of the electric vehicle sector.

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
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Publication History

Published: 13 May 2024

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Author Tags

  1. Ant Colony Optimization
  2. Charge scheduling optimization
  3. Electric Vehicles (EVs)
  4. Environmental benefits
  5. Genetic Algorithms
  6. Grid stability
  7. Meta-heuristic techniques
  8. Offline optimizations
  9. Particle Swarm Optimization. Simulated Annealing
  10. Power grid integration
  11. Power quality,Hybrid methods,Real-time environments
  12. Technological advancements

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