IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i9p740-d78054.html
   My bibliography  Save this article

Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains

Author

Listed:
  • Di Guo

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Changqing Du

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Fuwu Yan

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China)

Abstract

During the mode transition from the pure electric propulsion mode to the hybrid propulsion mode, clutch-based pre-transmission parallel hybrid electric vehicles are subject to drivability issues. These issues originate from the fact that in the clutch-based pre-transmission parallel hybrid powertrain (CPPHP) configuration, the clutch connects the engine and the motor. Without a carefully designed mode transition control that coordinates the engine torque, clutch torque and motor torque, torque sluggishness and surges occur during the mode transition, and residual torque oscillation occurs after the mode transition. In this paper, a discrete-time model predictive control (DMPC)-based controller is proposed to address these drivability-related issues. Modeling improvements and novel drivability-related indices and constraints are all taken into consideration in the design of the discrete-time model predictive controller. Furthermore, by using discrete-time Laguerre functions and introducing the equilibrium state and the ranking of constraints, an explicit solution of the discrete-time model predictive controller is obtained. The calculation results demonstrate that the proposed controller can ensure a smooth and rapidly decaying torque difference during the mode transition, alleviating the residual torque oscillation after the mode transition and guaranteeing that the mode transition is completed within an acceptable duration.

Suggested Citation

  • Di Guo & Changqing Du & Fuwu Yan, 2016. "Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains," Energies, MDPI, vol. 9(9), pages 1-31, September.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:740-:d:78054
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/9/740/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/9/740/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jixiang Fan & Jiangyan Zhang & Tielong Shen, 2015. "Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 8(9), pages 1-23, September.
    2. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    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. Tobias Nüesch & Alberto Cerofolini & Giorgio Mancini & Nicolò Cavina & Christopher Onder & Lino Guzzella, 2014. "Equivalent Consumption Minimization Strategy for the Control of Real Driving NOx Emissions of a Diesel Hybrid Electric Vehicle," Energies, MDPI, vol. 7(5), pages 1-31, May.
    5. Bishop, Justin D.K. & Martin, Niall P.D. & Boies, Adam M., 2014. "Cost-effectiveness of alternative powertrains for reduced energy use and CO2 emissions in passenger vehicles," Applied Energy, Elsevier, vol. 124(C), pages 44-61.
    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. Zhao, Chen & Zu, Bingfeng & Xu, Yuliang & Wang, Zhen & Zhou, Jianwei & Liu, Lina, 2020. "Design and analysis of an engine-start control strategy for a single-shaft parallel hybrid electric vehicle," Energy, Elsevier, vol. 202(C).
    2. Kyuhyun Sim & Sang-Min Oh & Ku-Young Kang & Sung-Ho Hwang, 2017. "A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 10(7), pages 1-15, 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. Yuping Zeng & Yang Cai & Guiyue Kou & Wei Gao & Datong Qin, 2018. "Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    2. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    3. Yuping Zeng & Yang Cai & Changbao Chu & Guiyue Kou & Wei Gao, 2018. "Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 11(7), pages 1-29, July.
    4. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    5. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.
    6. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    7. 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.
    8. Johannes Schalk & Harald Aschemann, 2016. "A Causal and Real-Time Capable Power Management Algorithm for Off-Highway Hybrid Propulsion Systems," Energies, MDPI, vol. 10(1), pages 1-17, December.
    9. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    10. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    11. 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.
    12. Da Wang & Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Feng Xiao, 2018. "Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process," Energies, MDPI, vol. 11(5), pages 1-20, April.
    13. 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).
    14. Jing Lian & Shuang Liu & Linhui Li & Xuanzuo Liu & Yafu Zhou & Fan Yang & Lushan Yuan, 2017. "A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)," Energies, MDPI, vol. 10(1), pages 1-18, January.
    15. Hung, Yi-Hsuan & Tung, Yu-Ming & Chang, Chun-Hsin, 2016. "Optimal control of integrated energy management/mode switch timing in a three-power-source hybrid powertrain," Applied Energy, Elsevier, vol. 173(C), pages 184-196.
    16. Di Battista, D. & Cipollone, R., 2016. "Experimental and numerical assessment of methods to reduce warm up time of engine lubricant oil," Applied Energy, Elsevier, vol. 162(C), pages 570-580.
    17. Hanho Son & Hyunsoo Kim, 2016. "Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses," Energies, MDPI, vol. 9(6), pages 1-18, May.
    18. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    19. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    20. 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.

    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:9:y:2016:i:9:p:740-:d:78054. 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.