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Dynamic Electric Vehicle Charging Optimization Model Based on PSO and GA Algorithms

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Communications, Signal Processing, and Systems (CSPS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1033))

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Abstract

The present paper deals with an overview of particle swarm algorithms and hybrid genetic-particle swarm algorithms, and a comparison between the related convergence speed and correlation error. The work includes also a dynamic mutic-objective charging model that is important for the security, stability, and economics of the smart grids. Finally, a brief analysis of GA-PSO multi-objective electric vehicle charging dynamic optimization is reported.

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References

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Acknowledgements

This work is supported by the Scientific Research Project of Tianjin Educational Committee (2022KJ010).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, 300387, China

    Xiaocheng Wang, Yuan Yin, Haogang Ma & Zelong Li

Authors
  1. Xiaocheng Wang
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  2. Yuan Yin
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  3. Haogang Ma
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  4. Zelong Li
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Corresponding author

Correspondence to Xiaocheng Wang .

Editor information

Editors and Affiliations

  1. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  2. Inovative Parking Building, Room B410, Dalian University of Technology, Dalian, China

    Xin Liu

  3. Sci & Tech, DianHang Bldg, Rm 321, Dalian Maritime Univ, Sch of Info, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, X., Yin, Y., Ma, H., Li, Z. (2024). Dynamic Electric Vehicle Charging Optimization Model Based on PSO and GA Algorithms. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_48

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  • DOI: https://doi.org/10.1007/978-981-99-7502-0_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7555-6

  • Online ISBN: 978-981-99-7502-0

  • eBook Packages: EngineeringEngineering (R0)

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