IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1835-d118424.html
   My bibliography  Save this article

On the Feasibility of Independently Controllable Transmissions

Author

Listed:
  • Milan Perkušić

    (Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Damir Jelaska

    (Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Srdjan Podrug

    (Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Vjekoslav Tvrdić

    (Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

Abstract

The present research is carried out to determine whether the independently controllable transmission (ICTs) are able to transmit the energy/power from the arbitrary variable speed input shaft to the variable or constant speed of the output shaft, independent of each other and without a control system. For this purpose, the novel ICT proposed in this study has the required features and is simpler than those proposed so far. However, this ICT and any other proposed ICT, while being feasible from a kinematic point of view, results in the conclusion that the transmissions are practically inapplicable, as will be demonstrated from the detailed power flow analysis carried out and presented in this paper. This is because either one shaft, the so called “free shaft”, has variable uncontrollable speed, or a couple of the shafts have powers close to zero.

Suggested Citation

  • Milan Perkušić & Damir Jelaska & Srdjan Podrug & Vjekoslav Tvrdić, 2017. "On the Feasibility of Independently Controllable Transmissions," Energies, MDPI, vol. 10(11), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1835-:d:118424
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1835/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1835/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    2. Francesco Bottiglione & Stefano De Pinto & Giacomo Mantriota & Aldo Sorniotti, 2014. "Energy Consumption of a Battery Electric Vehicle with Infinitely Variable Transmission," Energies, MDPI, vol. 7(12), pages 1-21, December.
    3. Hanho Son & Kyusik Park & Sungho Hwang & Hyunsoo Kim, 2017. "Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses," Energies, MDPI, vol. 10(4), pages 1-18, March.
    4. Yong Zhang & Xuerui Ma & Chengliang Yin & Shifei Yuan, 2016. "Development and Simulation of a Type of Four-Shaft ECVT for a Hybrid Electric Vehicle," Energies, MDPI, vol. 9(3), pages 1-20, February.
    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. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    2. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    3. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    4. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    5. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    6. 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.
    7. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    8. Rahman, Imran & Vasant, Pandian M. & Singh, Balbir Singh Mahinder & Abdullah-Al-Wadud, M. & Adnan, Nadia, 2016. "Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1039-1047.
    9. 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).
    10. 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).
    11. Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    12. Ruan, Jiageng & Walker, Paul & Zhang, Nong, 2016. "A comparative study energy consumption and costs of battery electric vehicle transmissions," Applied Energy, Elsevier, vol. 165(C), pages 119-134.
    13. Jacek Pielecha & Kinga Skobiej & Przemyslaw Kubiak & Marek Wozniak & Krzysztof Siczek, 2022. "Exhaust Emissions from Plug-in and HEV Vehicles in Type-Approval Tests and Real Driving Cycles," Energies, MDPI, vol. 15(7), pages 1-38, March.
    14. Xiangyang Xu & Xiaoxiao Wu & Mick Jordan & Peng Dong & Yang Liu, 2018. "Coordinated Engine-Start Control of Single-Motor P2 Hybrid Electric Vehicles with Respect to Different Driving Situations," Energies, MDPI, vol. 11(1), pages 1-23, January.
    15. Ruan, Jiageng & Walker, Paul D. & Watterson, Peter A. & Zhang, Nong, 2016. "The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle," Applied Energy, Elsevier, vol. 183(C), pages 1240-1258.
    16. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    17. Polychronis Spanoudakis & Nikolaos C. Tsourveloudis & Lefteris Doitsidis & Emmanuel S. Karapidakis, 2019. "Experimental Research of Transmissions on Electric Vehicles’ Energy Consumption," Energies, MDPI, vol. 12(3), pages 1-15, January.
    18. Chiwoong Song & Dongsuk Kum & Kyung-Soo Kim, 2018. "Feasibility Analysis and Performance Evaluation of a Novel Power-Split Flywheel Hybrid Vehicle," Energies, MDPI, vol. 11(7), pages 1-25, July.
    19. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    20. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).

    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:10:y:2017:i:11:p:1835-:d:118424. 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.