IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1538-d152248.html
   My bibliography  Save this article

A Power Coupling System for Electric Tracked Vehicles during High-Speed Steering with Optimization-Based Torque Distribution Control

Author

Listed:
  • Hong Huang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Li Zhai

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Zeda Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

It is significant to improve the steering maneuverability of dual-motor drive tracked vehicles (2MDTVs), which have wide applications in the tracked vehicle industry. In this paper, we focus on the problem of insufficient propulsion motor power during high-speed steering. Some correction formulas are introduced to improve the accuracy of the mathematical model. A steering coupling system and an optimization-based torque distribution control strategy is adopted to improve the lateral stability of the vehicle. The 2MDTV model and the proposed control strategy are built in the multi-body software RecurDyn and the control software Matlab/Simulink, respectively. According to the real-time steering simulation by the hardware-in-the-loop (HIL) method, the 2MDTV with the coupling device outputs more power during high-speed steering. The results show the speed during steering is quite high though, the stability of the vehicle can be achieved due to using the torque distribution strategy, and the steering maneuverability of the vehicle is also improved.

Suggested Citation

  • Hong Huang & Li Zhai & Zeda Wang, 2018. "A Power Coupling System for Electric Tracked Vehicles during High-Speed Steering with Optimization-Based Torque Distribution Control," Energies, MDPI, vol. 11(6), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1538-:d:152248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1538/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/6/1538/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li Zhai & Hong Huang & Steven Kavuma, 2017. "Investigation on a Power Coupling Steering System for Dual-Motor Drive Tracked Vehicles Based on Speed Control," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. Wei, Shouyang & Zou, Yuan & Sun, Fengchun & Christopher, Onder, 2017. "A pseudospectral method for solving optimal control problem of a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 194(C), pages 588-595.
    3. Wang, Hong & Huang, Yanjun & Khajepour, Amir & He, Hongwen & Cao, Dongpu, 2017. "A novel energy management for hybrid off-road vehicles without future driving cycles as a priori," Energy, Elsevier, vol. 133(C), pages 929-940.
    4. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xizheng Guo & Jiaqi Yuan & Yiguo Tang & Xiaojie You, 2018. "Hardware in the Loop Real-time Simulation for the Associated Discrete Circuit Modeling Optimization Method of Power Converters," Energies, MDPI, vol. 11(11), pages 1-14, November.
    2. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.

    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. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    2. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    3. Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    4. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    6. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    7. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    8. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    9. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    10. Tri Cuong Do & Hoai Vu Anh Truong & Hoang Vu Dao & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2019. "Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source," Energies, MDPI, vol. 12(22), pages 1-24, November.
    11. Sun, Wenjing & Zou, Yuan & Zhang, Xudong & Guo, Ningyuan & Zhang, Bin & Du, Guodong, 2022. "High robustness energy management strategy of hybrid electric vehicle based on improved soft actor-critic deep reinforcement learning," Energy, Elsevier, vol. 258(C).
    12. Wu, Changcheng & Ruan, Jiageng & Cui, Hanghang & Zhang, Bin & Li, Tongyang & Zhang, Kaixuan, 2023. "The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid ," Energy, Elsevier, vol. 262(PB).
    13. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    14. Zhang, Haoxiang & Wang, Feng & Lin, Zichang & Xu, Bing, 2023. "Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension," Applied Energy, Elsevier, vol. 351(C).
    15. Anwar, Hamza & Vishwanath, Aashrith & Ahmed, Qadeer & Chunodkar, Apurva, 2023. "Comprehensive energy footprint benchmarking of commercial electrified powertrains," Applied Energy, Elsevier, vol. 345(C).
    16. Jau-Woei Perng & Yi-Horng Lai, 2016. "Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller," Energies, MDPI, vol. 9(4), pages 1-15, April.
    17. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    18. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    19. Peng, Hui & Wang, Junzheng & Shen, Wei & Shi, Dawei & Huang, Yuan, 2019. "Compound control for energy management of the hybrid ultracapacitor-battery electric drive systems," Energy, Elsevier, vol. 175(C), pages 309-319.
    20. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.

    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:11:y:2018:i:6:p:1538-:d:152248. 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.