Research on the Layout and Robustness Testing of Electric Vehicle Battery Swap Stations Based on Graph Neural Networks: A Case Study of the Main Urban Area of Nanchang City School of Traffic and Transportation Engineering, East China

Format:

Book

Conference/Event

Author/Creator:

Zeng, Yi, author.

Contributor:

Yi, Xinyu

Conference Name:

2025 5th International Conference on Smart City Engineering and Public Transportation (SCEPT2025) (2025-03-28 : Beijing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

With the rapid increase in the number of electric vehicles, the rational placement of battery swapping stations has become a critical issue in optimizing urban transportation infrastructure. This paper proposes a site selection optimization method based on Graph Neural Networks (GNN). By constructing an urban transportation graph model grounded in Points of Interest (POI) and road traffic data, the method analyzes battery swapping station layout plans and validates their robustness and scalability. Taking the main urban area of Nanchang City as a case study, the research integrates data on POI distribution and land-use functional diversity within buffer zones to construct a graph structure. It then employs GNN for node classification to identify optimal battery swapping station locations. Experimental results show that, compared to traditional methods, the proposed approach improves site selection accuracy by 15% and enhances optimization efficiency by 20%. This method can provide efficient and precise layout solutions in complex urban environments. Additionally, sensitivity analysis and robustness testing confirm the model's stability and reliability under noisy data and dynamic traffic conditions

Notes:

Vendor supplied data

Publisher Number:

2025-99-0008

Access Restriction:

Restricted for use by site license