Abstract
The distance of the electric vehicle driving to the charging position is one of them, and reducing the loss is the primary research purpose. Query data to get road node distance, traffic flow and other information, charging demand can be obtained by analyzing the above data. After getting the necessary information, the ordinary particle algorithm is used to simulate the model. It is found that the ordinary particle algorithm has some shortcomings in the optimization, which may lead to the result is not the optimal solution. In order to obtain a more reasonable solution, the catfish particle algorithm and mutation particle algorithm are used to solve the location model. In order to compare the advantages and disadvantages of the three algorithms in the results, software simulation is adopted, The simulation software is MATLAB; Choose the charging station distribution coverage model of mutation particle algorithm with better optimization effect, and get the final model by integrating other conditions. In this paper, the feasibility and resource saving are considered to make the experimental results more reasonable.
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Chen, D., Zhou, M., Cui, Y., Mao, W., Zhu, D., Wang, Y. (2022). Location of Electric Vehicle Charging Station Based on Particle Swarm Optimization. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham. https://doi.org/10.1007/978-3-030-89511-2_127
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DOI: https://doi.org/10.1007/978-3-030-89511-2_127
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