IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223026452.html
   My bibliography  Save this article

Study on low-frequency torsional vibration suppression of integrated electric drive system considering nonlinear factors

Author

Listed:
  • Hu, Jianjun
  • Guo, Qi
  • Sun, Zhicheng
  • Yang, Dianzhao

Abstract

The integrated electric drive system (IEDS) is widely used in battery electric vehicles due to its advantages of high power density, small size, and low cost. However, on account of the electromechanical coupling between its subsystems, the problem of high torsional vibration is brought about. Therefore, based on the establishment of the electromechanical coupling model of IEDS and the analysis of the influence of time-varying mesh stiffness and backlash on the system characteristics, this paper proposes a double-layer optimization control strategy of IEDS torsional vibration basis of the torque command optimization and the principle of minimum error of instantaneous current tracking. By adjusting the motor torque command in real-time and reducing the error between the actual current and the target current, the oscillation amplitude of IEDS under Tip in/out condition is reduced by 63.7%, and the time required to achieve stability is shortened by 79.04% and 76.90% respectively. The simulation results show that the proposed control strategy greatly reduces the torsional vibration of IEDS and improves the comfort of the vehicle.

Suggested Citation

  • Hu, Jianjun & Guo, Qi & Sun, Zhicheng & Yang, Dianzhao, 2023. "Study on low-frequency torsional vibration suppression of integrated electric drive system considering nonlinear factors," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026452
    DOI: 10.1016/j.energy.2023.129251
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223026452
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, LiPeng & Liu, Wei & Qi, Bingnan, 2019. "Innovation design and optimization management of a new drive system for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 186(C).
    2. Tang, Xiaolin & Zhang, Dejiu & Liu, Teng & Khajepour, Amir & Yu, Haisheng & Wang, Hong, 2019. "Research on the energy control of a dual-motor hybrid vehicle during engine start-stop process," Energy, Elsevier, vol. 166(C), pages 1181-1193.
    3. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    4. Hu, Jianjun & Deng, Chenghao & Yang, Dianzhao & Yang, Ying & Jia, Meixia, 2022. "Optimal energy consumption and torque fluctuation control of integrated electric drive system based on mechanical-electromagnetic-thermal coupling characteristics," Energy, Elsevier, vol. 247(C).
    5. Mesloub, H. & Boumaaraf, R. & Benchouia, M.T. & Goléa, A. & Goléa, N. & Srairi, K., 2020. "Comparative study of conventional DTC and DTC_SVM based control of PMSM motor — Simulation and experimental results," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 296-307.
    6. Liu, Qin & Zhang, Wencan & Zhang, Zhongbo & Qin, Qichao, 2022. "A drive system global control strategy for electric vehicle based on optimized acceleration curve," Energy, Elsevier, vol. 248(C).
    7. Yang, Jian & Liu, Bo & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin, 2023. "Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation," Energy, Elsevier, vol. 263(PC).
    8. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.
    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. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    2. 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.
    3. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Jia, Jinda & Shan, Xiaobiao & Upadrashta, Deepesh & Xie, Tao & Yang, Yaowen & Song, Rujun, 2020. "An asymmetric bending-torsional piezoelectric energy harvester at low wind speed," Energy, Elsevier, vol. 198(C).
    5. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    6. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    7. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    8. Xing, Yazhou & Zhang, Su & Wen, Peng & Shao, Limin & Rouyendegh, Babak Daneshvar, 2020. "Load prediction in short-term implementing the multivariate quantile regression," Energy, Elsevier, vol. 196(C).
    9. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    10. Ibrahim Farouk Bouguenna & Ahmed Tahour & Ralph Kennel & Mohamed Abdelrahem, 2021. "Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems," Energies, MDPI, vol. 14(6), pages 1-23, March.
    11. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
    12. Chung, Cheng-Ta & Wu, Chien-Hsun & Hung, Yi-Hsuan, 2021. "A design methodology for selecting energy-efficient compound split e-CVT hybrid systems with planetary gearsets based on electric circulation," Energy, Elsevier, vol. 230(C).
    13. Kahourzade, Solmaz & Mahmoudi, Amin & Roshandel, Emad & Cao, Zhi, 2021. "Optimal design of Axial-Flux Induction Motors based on an improved analytical model," Energy, Elsevier, vol. 237(C).
    14. Chung, Cheng-Ta & Wu, Chien-Hsun & Hung, Yi-Hsuan, 2020. "Evaluation of driving performance and energy efficiency for a novel full hybrid system with dual-motor electric drive and integrated input- and output-split e-CVT," Energy, Elsevier, vol. 191(C).
    15. Gao, Mingyuan & Wang, Yuan & Wang, Yifeng & Yao, Ye & Wang, Ping & Sun, Yuhua & Xiao, Jieling, 2020. "Modeling and experimental verification of a fractional damping quad-stable energy harvesting system for use in wireless sensor networks," Energy, Elsevier, vol. 190(C).
    16. Zhen Zhu & Yanpeng Yang & Dongqing Wang & Yingfeng Cai & Longhui Lai, 2022. "Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System," Agriculture, MDPI, vol. 12(3), pages 1-22, March.
    17. Kwon, Kihan & Lee, Jung-Hwan & Lim, Sang-Kil, 2023. "Optimization of multi-speed transmission for electric vehicles based on electrical and mechanical efficiency analysis," Applied Energy, Elsevier, vol. 342(C).
    18. Yang, Yalian & Li, Pengshuai & Pei, Huanxin & Zou, Yunge, 2022. "Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory," Energy, Elsevier, vol. 242(C).
    19. Yang Yang & Qiang He & Yongzheng Chen & Chunyun Fu, 2020. "Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor," Energies, MDPI, vol. 13(3), pages 1-21, February.
    20. Wang, Shuai & Wu, Xiuheng & Zhao, Xueyan & Wang, Shilong & Xie, Bin & Song, Zhenghe & Wang, Dongqing, 2023. "Co-optimization energy management strategy for a novel dual-motor drive system of electric tractor considering efficiency and stability," Energy, Elsevier, vol. 281(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:eee:energy:v:284:y:2023:i:c:s0360544223026452. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.