IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i2p177-d89388.html
   My bibliography  Save this article

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Author

Listed:
  • Kun Huang

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China
    Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Changle Xiang

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Yue Ma

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Weida Wang

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Reza Langari

    (Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA)

Abstract

Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor–generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor–generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ.

Suggested Citation

  • Kun Huang & Changle Xiang & Yue Ma & Weida Wang & Reza Langari, 2017. "Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission," Energies, MDPI, vol. 10(2), pages 1-18, February.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:177-:d:89388
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/2/177/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/2/177/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Changle Xiang & Yanzi Wang & Sideng Hu & Weida Wang, 2014. "A New Topology and Control Strategy for a Hybrid Battery-Ultracapacitor Energy Storage System," Energies, MDPI, vol. 7(5), pages 1-23, April.
    2. Wenchen Shen & Huilong Yu & Yuhui Hu & Junqiang Xi, 2016. "Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission," Energies, MDPI, vol. 9(3), pages 1-11, March.
    3. Ouyang, Minggao & Zhang, Weilin & Wang, Enhua & Yang, Fuyuan & Li, Jianqiu & Li, Zhongyan & Yu, Ping & Ye, Xiao, 2015. "Performance analysis of a novel coaxial power-split hybrid powertrain using a CNG engine and supercapacitors," Applied Energy, Elsevier, vol. 157(C), pages 595-606.
    4. Zhuang, Weichao & Zhang, Xiaowu & Ding, Yang & Wang, Liangmo & Hu, Xiaosong, 2016. "Comparison of multi-mode hybrid powertrains with multiple planetary gears," Applied Energy, Elsevier, vol. 178(C), pages 624-632.
    5. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    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. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Aroua, Ayoub & Lhomme, Walter & Redondo-Iglesias, Eduardo & Verbelen, Florian, 2022. "Fuel saving potential of a long haul heavy duty vehicle equipped with an electrical variable transmission," Applied Energy, Elsevier, vol. 307(C).
    3. Massimiliano Passalacqua & Damiano Lanzarotto & Matteo Repetto & Mario Marchesoni, 2017. "Advantages of Using Supercapacitors and Silicon Carbide on Hybrid Vehicle Series Architecture," Energies, MDPI, vol. 10(7), pages 1-14, 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. Zhuang, Weichao & Zhang, Xiaowu & Li, Daofei & Wang, Liangmo & Yin, Guodong, 2017. "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle," Applied Energy, Elsevier, vol. 204(C), pages 476-488.
    2. Cai, Y. & Ouyang, M.G. & Yang, F., 2017. "Impact of power split configurations on fuel consumption and battery degradation in plug-in hybrid electric city buses," Applied Energy, Elsevier, vol. 188(C), pages 257-269.
    3. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2020. "Velocity and energy trajectory prediction of electrified powertrain for look ahead control," Applied Energy, Elsevier, vol. 279(C).
    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. 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).
    6. Li, Wei & Jia, Zhijie & Zhang, Hongzhi, 2017. "The impact of electric vehicles and CCS in the context of emission trading scheme in China: A CGE-based analysis," Energy, Elsevier, vol. 119(C), pages 800-816.
    7. 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.
    8. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    9. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2020. "Comparison of four-wheel-drive hybrid powertrain configurations," Energy, Elsevier, vol. 209(C).
    10. Pei Zhang & Wangda Lu & Changqing Du & Jie Hu & Fuwu Yan, 2024. "A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network," Mathematics, MDPI, vol. 12(4), pages 1-26, February.
    11. Wenjian Yang & Changping Li, 2022. "Symmetry Detection and Topological Synthesis of Mechanisms of Powertrains," Energies, MDPI, vol. 15(13), pages 1-22, June.
    12. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    13. Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    14. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2023. "Evaluation of a Three-Parameter Gearshift Strategy for a Two-Speed Transmission System in Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-28, March.
    15. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    16. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    17. Fabio Orecchini & Adriano Santiangeli & Fabrizio Zuccari & Adriano Alessandrini & Fabio Cignini & Fernando Ortenzi, 2021. "Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model," Energies, MDPI, vol. 14(23), pages 1-22, December.
    18. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2020. "Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle," Energies, MDPI, vol. 13(19), pages 1-24, September.
    19. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
    20. Feng, Yanbiao & Dong, Zuomin, 2019. "Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement," Energy, Elsevier, vol. 189(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:2:p:177-:d:89388. 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.