IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p8225-d856463.html
   My bibliography  Save this article

Design Method for Hybrid Electric Vehicle Powertrain Configuration with a Single Motor

Author

Listed:
  • Bo Huang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Minghui Hu

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Li Zeng

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Guangshun Fu

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Qinglong Jia

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

Abstract

Existing design methods for hybrid power system configurations obtain new solutions based on experience, structure improvement or optimization, exhaustive searching, and the screening of schemes at the expense of less innovation and less efficiency. Furthermore, these methods lack mechanisms involving automotive theory to guide powertrain configuration design. In this study, a design method of configuration with a single motor based on basic schemes of speed and torque decoupling was proposed from the perspective of the hybrid electric vehicle fuel-saving mechanism. First, the coupling characteristics of speed and torque in the basic scheme were analyzed from four perspectives. Thereafter, new configurations that meet operation requirements were derived via configuration reconstruction, which combined the better basic schemes with brakes, clutches, and transmissions. A multidimensional evaluation and screening method based on dynamic performance, economic performance, and adaptability was built. A comparison of S-4 with Toyota Hybrid System, which was performed to demonstrate the feasibility of the design method, revealed that both configurations perform similarly in terms of economic performance, but the dynamic performance of the S-4 is greater by approximately 50%. The times required to attain 100 km/h from 0 km/h for THS and S-4 are 13.5 s and 6.69 s, respectively.

Suggested Citation

  • Bo Huang & Minghui Hu & Li Zeng & Guangshun Fu & Qinglong Jia, 2022. "Design Method for Hybrid Electric Vehicle Powertrain Configuration with a Single Motor," Sustainability, MDPI, vol. 14(13), pages 1-22, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8225-:d:856463
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/8225/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/13/8225/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shi, Dehua & Pisu, Pierluigi & Chen, Long & Wang, Shaohua & Wang, Renguang, 2016. "Control design and fuel economy investigation of power split HEV with energy regeneration of suspension," Applied Energy, Elsevier, vol. 182(C), pages 576-589.
    2. Ngoc-Tan Hoang & Hong-Sen Yan, 2017. "Configuration Synthesis of Novel Series-Parallel Hybrid Transmission Systems with Eight-Bar Mechanisms," Energies, MDPI, vol. 10(7), pages 1-15, 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. Claudia Violeta Pop & Daniel Fodorean & Dan-Cristian Popa, 2022. "Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications," Sustainability, MDPI, vol. 14(19), pages 1-23, September.

    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. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    2. Abdelkareem, Mohamed A.A. & Xu, Lin & Ali, Mohamed Kamal Ahmed & El-Daly, Abdel-Rahman B.M. & Hassan, Mohamed A. & Elagouz, Ahmed & Bo, Yang, 2019. "Analysis of the prospective vibrational energy harvesting of heavy-duty truck suspensions: A simulation approach," Energy, Elsevier, vol. 173(C), pages 332-351.
    3. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    4. Chen, Shi-An & Jiang, Xu-Dong & Yao, Ming & Jiang, Shun-Ming & Chen, Jinzhou & Wang, Ya-Xiong, 2020. "A dual vibration reduction structure-based self-powered active suspension system with PMSM-ball screw actuator via an improved H2/H∞ control," Energy, Elsevier, vol. 201(C).
    5. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    6. Pei, Huanxin & Hu, Xiaosong & Yang, Yalian & Tang, Xiaolin & Hou, Cong & Cao, Dongpu, 2018. "Configuration optimization for improving fuel efficiency of power split hybrid powertrains with a single planetary gear," Applied Energy, Elsevier, vol. 214(C), pages 103-116.
    7. Thanh-Tho Ho & Sheng-Jye Hwang, 2020. "Configuration Synthesis of Novel Hybrid Transmission Systems Using a Combination of a Ravigneaux Gear Train and a Simple Planetary Gear Train," Energies, MDPI, vol. 13(9), pages 1-24, May.
    8. Wenjian Yang & Yongtao Li & Rongjiang Cui & Hongmei Kang, 2023. "A New Tree Graph Method for Synthesizing Planetary Gear Trains of Vehicle Powertrains," Energies, MDPI, vol. 16(20), pages 1-27, October.
    9. Gao, Zepeng & Chen, Sizhong & Zhao, Yuzhuang & Liu, Zheng, 2019. "Numerical evaluation of compatibility between comfort and energy recovery based on energy flow mechanism inside electromagnetic active suspension," Energy, Elsevier, vol. 170(C), pages 521-536.
    10. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    11. Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
    12. Xu Hu & Jinwei Sun & Yisong Chen & Qiu Liu & Liang Gu, 2019. "Considering Well-to-Wheels Analysis in Control Design: Regenerative Suspension Helps to Reduce Greenhouse Gas Emissions from Battery Electric Vehicles," Energies, MDPI, vol. 12(13), pages 1-19, July.
    13. Xueying Lv & Yanju Ji & Huanyu Zhao & Jiabao Zhang & Guanyu Zhang & Liu Zhang, 2020. "Research Review of a Vehicle Energy-Regenerative Suspension System," Energies, MDPI, vol. 13(2), pages 1-14, January.
    14. Wang, Feng & Zhang, Jian & Xu, Xing & Cai, Yingfeng & Zhou, Zhiguang & Sun, Xiaoqiang, 2019. "A comprehensive dynamic efficiency-enhanced energy management strategy for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 247(C), pages 657-669.
    15. Abdelkareem, Mohamed A.A. & Xu, Lin & Ali, Mohamed Kamal Ahmed & Elagouz, Ahmed & Mi, Jia & Guo, Sijing & Liu, Yilun & Zuo, Lei, 2018. "Vibration energy harvesting in automotive suspension system: A detailed review," Applied Energy, Elsevier, vol. 229(C), pages 672-699.
    16. 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.
    17. Yu, Wei & Wang, Ruochen, 2019. "Development and performance evaluation of a comprehensive automotive energy recovery system with a refined energy management strategy," Energy, Elsevier, vol. 189(C).
    18. Long, Guimin & Ding, Fei & Zhang, Nong & Zhang, Jie & Qin, An, 2020. "Regenerative active suspension system with residual energy for in-wheel motor driven electric vehicle," Applied Energy, Elsevier, vol. 260(C).
    19. 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.
    20. Ngoc-Tan Hoang & Hong-Sen Yan, 2018. "On the Design of In-Wheel-Hub Motor Transmission Systems with Six-Link Mechanisms for Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-15, October.

    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:jsusta:v:14:y:2022:i:13:p:8225-:d:856463. 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.