IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6544-d909216.html
   My bibliography  Save this article

Design and Optimization on the Degree of Hybridization of Underground Hybrid Electric Trackless Rubber-Tyred Vehicle

Author

Listed:
  • Xiaoming Yuan

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
    China Coal Technology & Engineering Group Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China)

  • Yao Lu

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Jiusheng Bao

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Peixin Han

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Yan Yin

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Xu Wang

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

The explosion-proof diesel engine trackless rubber-tyred vehicle (TRTV) has the disadvantages of high fuel consumption and serious exhaust emissions, while the problems of insufficient power and short endurance limit the development of the explosion-proof battery trackless rubber-tyred vehicle. Hybrid technology can effectively reduce fuel consumption and emissions on the basis of ensuring sufficient power. Exploring the application of hybrid electric trackless rubber-tyred vehicle (HETRTV) has practical significance for coal mine auxiliary transportation. The degree of hybridization (DOH) will directly affect the performance and cost of TRTV, which needs to be focused in the development process. The effects of DOH on dynamic performance, fuel economy, emission performance, and cost were studied based on a simulation by ADVISOR, and the results were verified and analyzed by experiment. Compared with the flameproof diesel engine trackless vehicles, HETRTV with the optimal DOH exhausts has far less gas emissions. The engine fuel consumption and the equivalent fuel consumption of the vehicle are reduced by 33.9% and 12.5%, respectively. The results showed that in spite of a small increase in cost, the HETRTV with the optimal DOH can not only meet the driving requirements of underground working conditions but also greatly improve fuel economy and emission performance.

Suggested Citation

  • Xiaoming Yuan & Yao Lu & Jiusheng Bao & Peixin Han & Yan Yin & Xu Wang, 2022. "Design and Optimization on the Degree of Hybridization of Underground Hybrid Electric Trackless Rubber-Tyred Vehicle," Energies, MDPI, vol. 15(18), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6544-:d:909216
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6544/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/18/6544/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Song, Ziyou & Zhang, Xiaobin & Li, Jianqiu & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system," Energy, Elsevier, vol. 144(C), pages 393-403.
    2. Roberto Capata & Antonino Coccia, 2010. "Procedure for the Design of a Hybrid-Series Vehicle and the Hybridization Degree Choice," Energies, MDPI, vol. 3(3), pages 1-12, March.
    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. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    2. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    3. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    4. Jiang, Hongliang & Xu, Liangfei & Li, Jianqiu & Hu, Zunyan & Ouyang, Minggao, 2019. "Energy management and component sizing for a fuel cell/battery/supercapacitor hybrid powertrain based on two-dimensional optimization algorithms," Energy, Elsevier, vol. 177(C), pages 386-396.
    5. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    6. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    7. Bhattacharjee, Debraj & Ghosh, Tamal & Bhola, Prabha & Martinsen, Kristian & Dan, Pranab K., 2019. "Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance," Energy, Elsevier, vol. 183(C), pages 235-248.
    8. Du, Jiuyu & Zhang, Xiaobin & Wang, Tianze & Song, Ziyou & Yang, Xueqing & Wang, Hewu & Ouyang, Minggao & Wu, Xiaogang, 2018. "Battery degradation minimization oriented energy management strategy for plug-in hybrid electric bus with multi-energy storage system," Energy, Elsevier, vol. 165(PA), pages 153-163.
    9. Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2022. "Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources," Energies, MDPI, vol. 15(9), pages 1-18, May.
    10. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    11. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    12. Capata, Roberto & Sciubba, Enrico, 2013. "The LETHE© (Low Emissions Turbo-Hybrid Engine) city car of the University of Roma 1: Final proposed configuration," Energy, Elsevier, vol. 58(C), pages 178-184.
    13. Wu, Wei & Chuang, Bo-Neng & Hwang, Jenn-Jiang & Lin, Chien-Kung & Yang, Shu-Bo, 2019. "Techno-economic evaluation of a hybrid fuel cell vehicle with on-board MeOH-to-H2 processor," Applied Energy, Elsevier, vol. 238(C), pages 401-412.
    14. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    15. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    16. Wu, Yue & Huang, Zhiwu & Li, Dongjun & Li, Heng & Peng, Jun & Stroe, Daniel & Song, Ziyou, 2024. "Optimal battery thermal management for electric vehicles with battery degradation minimization," Applied Energy, Elsevier, vol. 353(PA).
    17. Ying Yang & Weige Zhang & Shaoyuan Wei & Zhenpo Wang, 2020. "Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram," Energies, MDPI, vol. 13(23), pages 1-21, November.
    18. Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2020. "Optimal Sizing of Fuel Cell Hybrid Power Sources with Reliability Consideration," Energies, MDPI, vol. 13(13), pages 1-18, July.
    19. Ioan Aschilean & Mihai Varlam & Mihai Culcer & Mariana Iliescu & Mircea Raceanu & Adrian Enache & Maria Simona Raboaca & Gabriel Rasoi & Constantin Filote, 2018. "Hybrid Electric Powertrain with Fuel Cells for a Series Vehicle," Energies, MDPI, vol. 11(5), pages 1-12, May.
    20. Derollepot, Romain & Vinot, Emmanuel, 2019. "Sizing of a combined series-parallel hybrid architecture for river ship application using genetic algorithm and optimal energy management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 248-263.

    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:gam:jeners:v:15:y:2022:i:18:p:6544-:d:909216. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.