A Robust RF-Based Wireless Charging System for Dockless Bike-Sharing

被引:2
|
作者
He, Shibo [1 ,2 ]
Hu, Kang [1 ]
Li, Songyuan [1 ]
Fu, Lingkun [1 ]
Gu, Chaojie [1 ]
Chen, Jiming [1 ]
机构
[1] Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China
[2] Key Lab Collaborat Sensing & Autonomous Unmanned S, Hangzhou 310015, Peoples R China
基金
中国国家自然科学基金;
关键词
Inductive charging; Radio frequency; Wireless sensor networks; Wireless communication; Sensors; Receivers; Delays; Dockless bike-sharing; wireless charging system; radio frequency; wireless power transfer; SET MULTICOVER; POWER TRANSFER; ENERGY; AUTHENTICATION; INFORMATION; ALGORITHMS; NETWORKS; COVERAGE; SCHEME;
D O I
10.1109/TMC.2023.3255980
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this article, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve 85% of the optimal solution.
引用
收藏
页码:2395 / 2406
页数:12
相关论文
共 50 条
  • [41] The last mile matters: Impact of dockless bike-sharing services on traffic congestion
    Huang, Ganxiang
    Xu, Di
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2023, 121
  • [42] A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction
    Luo, Kang
    Song, Yancun
    Shi, Ziyi
    Yu, Qing
    Wang, Guanqi
    Shen, Yonggang
    Computers and Industrial Engineering, 2024, 198
  • [43] Travel satisfaction with dockless bike-sharing: Trip stages, attitudes and the built environment
    Chen, Zheyan
    van Lierop, Dea
    Ettema, Dick
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2022, 106
  • [44] The Impact of Dockless Bike-Sharing on Public Transit: A Case Study of Shanghai, China
    Yun, Meiping
    Wei, Shuang
    Ma, Yue
    CICTP 2021: ADVANCED TRANSPORTATION, ENHANCED CONNECTION, 2021, : 955 - 965
  • [45] Citywide Bike Usage Prediction in a Bike-Sharing System
    Li, Yexin
    Zheng, Yu
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 32 (06) : 1079 - 1091
  • [46] Exploring spatio-temporal pattern heterogeneity of dockless bike-sharing system: Links with cycling environment
    Gao, Wei
    Hu, Xiaowei
    Wang, Naihui
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2023, 117
  • [47] System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China
    Yang, Tianjian
    Li, Ye
    Zhou, Simin
    SUSTAINABILITY, 2019, 11 (06)
  • [48] Measuring the vulnerability of bike-sharing system
    Zhang, Liye
    Xiao, Zhe
    Ren, Shen
    Qin, Zheng
    Goh, Rick Siow Mong
    Song, Jie
    TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2022, 163 : 353 - 369
  • [49] Traffic Prediction in a Bike-Sharing System
    Li, Yexin
    Zheng, Yu
    Zhang, Huichu
    Chen, Lei
    23RD ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2015), 2015,
  • [50] A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system
    Ai, Yi
    Li, Zongping
    Gan, Mi
    Zhang, Yunpeng
    Yu, Daben
    Chen, Wei
    Ju, Yanni
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (05): : 1665 - 1677