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Optimization of Charging/Battery-Swap Station Location of Electric Vehicles with an Improved Genetic Algorithm-Based Model

Bida Zhang1,*, Qiang Yan1, Hairui Zhang2, Lin Zhang3

1 Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 PLA Army Academy of Artillery and Air Defense, Zhengzhou, 450000, China
3 Beijing Information Science and Technology University, Beijing, 100192, China

* Corresponding Author: Bida Zhang. Email:

(This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1177-1194.


The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem. Considering the differences between these two modes of power replenishment, we constructed a joint location-planning model to minimize construction and operation costs, user costs, and user satisfaction-related penalty costs. We designed an improved genetic algorithm that changes the crossover rate using the fitness value, memorizes, and transfers excellent genes. In addition, the present model addresses the problem of “premature convergence” in conventional genetic algorithms. A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency. The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations. Instead, optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit. The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities. Hence, it is of both theoretical and practical value.


Cite This Article

Zhang, B., Yan, Q., Zhang, H., Zhang, L. (2023). Optimization of Charging/Battery-Swap Station Location of Electric Vehicles with an Improved Genetic Algorithm-Based Model. CMES-Computer Modeling in Engineering & Sciences, 134(2), 1177–1194.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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