IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1148-d144599.html
   My bibliography  Save this article

Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine

Author

Listed:
  • Jinlong Hong

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Liangchun Zhao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Yulong Lei

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Bingzhao Gao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

Abstract

The great development of engine technologies can help to improve the engine characteristics and performance: a better thermal efficiency and an extending fuel economy area, which will subsequently decrease the fuel consumption and thus influence the overall architecture of the vehicle. In this paper, an investigation is carried out to assess the influence of the high-efficiency engine on the transmission gear numbers. First, according to the relevant studies and the integration of the advanced engine technology, a future engine fuel consumption map is obtained, based on which, the preliminary simulations are applied to explore the best match between the transmission and the proposed future engine from the perspective of fuel consumption. The simulation results indicate that the transmission with four gears is the best option to match the future engine while maintaining good fuel economy and meeting the driving demands. Then, based on this conclusion, a new hybrid powertrain architecture, which includes four gears for the engine, is introduced and analyzed in detail, with the advantage of seamless gear shift due to the compensation torque of the motor. Finally, to further examine the fuel economy and gear shift quality of the proposed powertrain, the dynamic model is established and the simulation results demonstrate that the new powertrain architecture shows a good fuel consumption performance and the gear shift process can be achieved without power interruption.

Suggested Citation

  • Jinlong Hong & Liangchun Zhao & Yulong Lei & Bingzhao Gao, 2018. "Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine," Energies, MDPI, vol. 11(5), pages 1-23, May.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1148-:d:144599
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1148/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/5/1148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    2. Ali Solouk & Mahdi Shahbakhti, 2016. "Energy Optimization and Fuel Economy Investigation of a Series Hybrid Electric Vehicle Integrated with Diesel/RCCI Engines," Energies, MDPI, vol. 9(12), pages 1-23, December.
    3. Tobias Nüesch & Philipp Elbert & Michael Flankl & Christopher Onder & Lino Guzzella, 2014. "Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs," Energies, MDPI, vol. 7(2), pages 1-23, February.
    4. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    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. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    2. Paykani, Amin & Garcia, Antonio & Shahbakhti, Mahdi & Rahnama, Pourya & Reitz, Rolf D., 2021. "Reactivity controlled compression ignition engine: Pathways towards commercial viability," Applied Energy, Elsevier, vol. 282(PA).

    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. 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).
    2. 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).
    3. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    4. Ruan, Jiageng & Wu, Changcheng & Liang, Zhaowen & Liu, Kai & Li, Bin & Li, Weihan & Li, Tongyang, 2023. "The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode," Energy, Elsevier, vol. 269(C).
    5. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    6. K. S. Reddy & S. Aravindhan & Tapas K. Mallick, 2017. "Techno-Economic Investigation of Solar Powered Electric Auto-Rickshaw for a Sustainable Transport System," Energies, MDPI, vol. 10(6), pages 1-15, May.
    7. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    8. 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.
    9. Bravo, Rafael Rivelino Silva & De Negri, Victor Juliano & Oliveira, Amir Antonio Martins, 2018. "Design and analysis of a parallel hydraulic – pneumatic regenerative braking system for heavy-duty hybrid vehicles," Applied Energy, Elsevier, vol. 225(C), pages 60-77.
    10. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    11. Guangli Zhou & Fei Huang & Wenbing Liu & Chunling Zhao & Yangkai Xiang & Hanbing Wei, 2022. "Comprehensive Control Strategy of Fuel Consumption and Emissions Incorporating the Catalyst Temperature for PHEVs Based on DRL," Energies, MDPI, vol. 15(20), pages 1-18, October.
    12. 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.
    13. 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.
    14. José M. Cansino & Antonio Sánchez-Braza & Teresa Sanz-Díaz, 2018. "Policy Instruments to Promote Electro-Mobility in the EU28: A Comprehensive Review," Sustainability, MDPI, vol. 10(7), pages 1-27, July.
    15. 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.
    16. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    17. 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.
    18. Zhang, Zhenying & Wang, Jiayu & Feng, Xu & Chang, Li & Chen, Yanhua & Wang, Xingguo, 2018. "The solutions to electric vehicle air conditioning systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 443-463.
    19. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    20. Wasbari, F. & Bakar, R.A. & Gan, L.M. & Tahir, M.M. & Yusof, A.A., 2017. "A review of compressed-air hybrid technology in vehicle system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 935-953.

    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:5:p:1148-:d:144599. 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.