Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (12): 71-80.doi: 10.11896/jsjkx.250200010

• Database & Big Data & Data Science • Previous Articles     Next Articles

Cross-regional Battery Swapping Station Deployment Algorithm Based on Intelligent E-scooterFlow Generation

CHEN Jiayi1, GU Chengyi1, ZHOU Jihua2, ZHAO Tao2,3, WANG Shuangchao4, ZHU Mingxing5, XIANG Chaocan1   

  1. 1 College of Computer Science, Chongqing University, Chongqing 401331, China
    2 School of Software, College of Computer and Information Science, Southwest University, Chongqing 400715, China
    3 School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
    4 China Mobile IOT Company Limited, Chongqing 401121, China
    5 Aerospace New Generation Communications Company Limited, Chongqing 401332, China
  • Received:2025-02-05 Revised:2025-06-22 Online:2025-12-15 Published:2025-12-09
  • About author:CHEN Jiayi,born in 2000,postgra-duate.Her main research interests include urban computing and edge computing.
    ZHAO Tao,born in 1983,professor,master’s supervisor.His main research interests include IoT networks and broadband intelligent communications.

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Abstract: Battery swapping services are becoming increasingly popular as intelligent E-scooters emerge as a primary mode of short-distance transportation.As a result,companies providing battery swapping services are expanding their operations in urban regions.During expansion,companies prioritize deploying battery swapping stations in regions with higher user activity,as indicated by intelligent E-scooter flow.However,the lack of operational data in new regions makes data-driven deployment optimization approaches less effective,creating a dilemma of “data scarcity-deployment difficulty-lack of data acquisition”.To address this issue,this paper proposes a cross-regional battery swapping station deployment algorithm based on intelligent E-scooter flow ge-neration.Firstly,a regional flow generation model based on a denoising diffusion probabilistic model is constructed to capture the spatiotemporal characteristics of flow and generate synthetic flow data for target regions using data from already deployed regions.Then,the generated flow is incorporated into a deployment optimization problem,modeled to maximize the overall benefit of station deployment.Finally,a cross-regional deployment strategy is derived using an adaptive genetic algorithm.The proposed algorithm is evaluated using a real-world dataset from Chengdu,Sichuan Province.Experimental results demonstrate the effectiveness of the proposed solution.

Key words: Battery swapping model, Battery swapping station deployment, Regional flow, Denoising diffusion probabilistic model

CLC Number: 

  • TP393
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