| Yang Xi (杨 茜)*,Tang Erdi**,Liu Qinkun*,Wang Liang*.[J].高技术通讯(英文),2026,32(3):289~297 |
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| Optimal site selection for new energy vehicle charging stations using a SAC-PSO algorithm |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 03. 008 |
| 中文关键词: |
| 英文关键词: new energy vehicles, charging station site selection, simulated annealing chaotic particle swarm optimization algorithm, Voronoi diagram, Floyd-Warshall algorithm, objective optimization |
| 基金项目: |
| Author Name | Affiliation | | Yang Xi (杨 茜)* | (*Power China Northwest Engineering Corporation Limited, Xi’an 710065, P. R. China)
(**School of Robotics and Embodied Intelligence, Chang’an University, Xi’an 710064, P. R. China) | | Tang Erdi** | | | Liu Qinkun* | | | Wang Liang* | |
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| 中文摘要: |
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| 英文摘要: |
| This study tackles the optimal site selection for new energy vehicle (NEV) charging stations in
urban areas. A multi-objective optimization framework is proposed, which first employs Voronoi dia-
grams and the Floyd-Warshall algorithm to delineate service zones and compute network-based dis-
tances. A siting model is then formulated to minimize costs, encompassing construction, operation,
grid loss, and user expenses. To solve this complex model, an improved simulated annealing chaotic
particle swarm optimization ( SAC-PSO ) algorithm is developed. It utilizes chaotic mapping to en-
hance exploration and incorporates a simulated annealing mechanism to escape local optima, thus
preserving strong global search capabilities. A case study of Xi’an’s central urban district demon-
strates the method’s effectiveness. The optimal plan proposes building 5 stations with a total cost of
10. 112 × 10 6 CNY ( Chinese Yuan ), reducing cost by 15. 2% compared to standard PSO. This ap-
proach provides a robust decision-making tool for urban charging infrastructure planning. |
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