IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v375y2024ics0306261924013266.html
   My bibliography  Save this article

A coreless track-type seamless wireless charging system using co-planar wires enabling quasi-free planar movements for mobile logistics robots

Author

Listed:
  • Jo, Hyunkyeong
  • Seo, Seoktae
  • Kim, Jungho
  • Bien, Franklin

Abstract

The adoption of mobile robots in industrial automation is rapidly expanding, driven by not only economic advantages but also considerations for environmental conservation and worker safety. Dynamic wireless power transfer systems for mobile logistics robots emerge as a promising technology, enhancing the feasibility of battery-less mobile robots while concurrently reducing energy and raw material consumptions associated with battery production. Recent research in track-powered mobile robots has advanced the achievement of uniform and reliable power transfer for moving vehicles. In this paper, we introduce an optimally designed transmitter and receiver for seamless charging, affording full degrees of freedom for vehicles within a two-dimensional space. The technique for adjusting the self-resistance of the receiver with a ferromagnetic core has demonstrated a dependable efficiency of nearly 90%, enabling the provision of 100 W of power, sufficient for mobile robot operations. The track length is 16 m, the most prolonged coreless track in the research field. Moreover, we present a communication-less feedback system for multiple vehicles and validate human safety near the robot through simulations and experiments. We anticipate that the proposed wireless charging system will play a fundamental role in promoting the widespread of mobile robots, effectively addressing economic, environmental, and societal challenges.

Suggested Citation

  • Jo, Hyunkyeong & Seo, Seoktae & Kim, Jungho & Bien, Franklin, 2024. "A coreless track-type seamless wireless charging system using co-planar wires enabling quasi-free planar movements for mobile logistics robots," Applied Energy, Elsevier, vol. 375(C).
  • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924013266
    DOI: 10.1016/j.apenergy.2024.123943
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924013266
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.123943?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ballestar, María Teresa & Díaz-Chao, Ángel & Sainz, Jorge & Torrent-Sellens, Joan, 2020. "Knowledge, robots and productivity in SMEs: Explaining the second digital wave," Journal of Business Research, Elsevier, vol. 108(C), pages 119-131.
    2. Chen, Zhibin & He, Fang & Yin, Yafeng, 2016. "Optimal deployment of charging lanes for electric vehicles in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 344-365.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Li, Daiyue & Jin, Yanhong & Cheng, Mingwang, 2024. "Unleashing the power of industrial robotics on firm productivity: Evidence from China," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 500-520.
    3. Agata Marcysiak & Żanna Pleskacz, 2021. "Determinants of digitization in SMEs," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(1), pages 300-318, September.
    4. Liu, Haoxiang & Wang, David Z.W., 2017. "Locating multiple types of charging facilities for battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 30-55.
    5. Alena Pauliková & Zdenka Gyurák Babeľová & Monika Ubárová, 2021. "Analysis of the Impact of Human–Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    6. Noruzoliaee, Mohamadhossein & Zou, Bo, 2022. "One-to-many matching and section-based formulation of autonomous ridesharing equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 72-100.
    7. Meintjes, Tiago & Castro, Rui & Pires, A.J., 2021. "Impact of vehicle charging on Portugal's national electricity load profile in 2030," Utilities Policy, Elsevier, vol. 73(C).
    8. Lamperti, Fabio, 2024. "Unlocking machine learning for social sciences: The case for identifying Industry 4.0 adoption across business restructuring events," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
    9. Cui, Shaohua & Ma, Xiaolei & Zhang, Mingheng & Yu, Bin & Yao, Baozhen, 2022. "The parallel mobile charging service for free-floating shared electric vehicle clusters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    10. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    11. Yan, Xiao-Yu & Yang, Shi-Chun & He, Hong & Tang, Tie-Qiao, 2018. "An optimization model for wireless power transfer system based on circuit simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 873-880.
    12. Zhao, Yantong & Said, Rusmawati & Ismail, Normaz Wana & Hamzah, Hanny Zurina, 2024. "Impact of industrial robot on labour productivity: Empirical study based on industry panel data," Innovation and Green Development, Elsevier, vol. 3(2).
    13. Cui, Huijie & Liang, Shangkun & Xu, Canyu & Junli, Yu, 2024. "Robots and analyst forecast precision: Evidence from Chinese manufacturing," International Review of Financial Analysis, Elsevier, vol. 94(C).
    14. Zoltan Csefalvay & Petros Gkotsis, 2020. "Global race for robotisation - Looking at the entire robotisation chain," JRC Research Reports JRC121184, Joint Research Centre.
    15. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    16. Dario Guarascio & Alessandro Piccirillo & Jelena Reljic, 2024. "Will robot replace workers? Assessing the impact of robots on employment and wages with meta-analysis," Working Papers in Public Economics 245, Department of Economics and Law, Sapienza University of Roma.
    17. Yudai Honma & Daisuke Hasegawa & Katsuhiro Hata & Takashi Oguchi, 2024. "Locational Analysis of In-motion Wireless Power Transfer System for Long-distance Trips by Electric Vehicles: Optimal Locations and Economic Rationality in Japanese Expressway Network," Networks and Spatial Economics, Springer, vol. 24(1), pages 261-290, March.
    18. Song, Jiatong & Li, Baicheng & Szeto, W.Y. & Zhan, Xingbin, 2024. "A station location design problem in a bike-sharing system with both conventional and electric shared bikes considering bike users’ roaming delay costs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    19. Saša Petković & Jelica Rastoka & Dragana Radicic, 2023. "Impact of Innovation and Exports on Productivity: Are There Complementary Effects?," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    20. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924013266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.