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Torque vectoring algorithm based on mechanical elastic electric wheels with consideration of the stability and economy

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  • Deng, Huifan
  • Zhao, Youqun
  • Feng, Shilin
  • Wang, Qiuwei
  • Zhang, Chenxi
  • Lin, Fen

Abstract

How to coordinate the relationship between stability and economy according to the current motion state of the vehicle is an urgent problem to be considered in torque distribution. To solve this problem, a novel torque vectoring algorithm is proposed based on a novel type of mechanical elastic electric wheel. The tire model of mechanical elastic wheel is identified based on Fibonacci tree. Considering the nonlinearity of tire force, a linear parameter-varying linear quadratic regulator upper controller based on a 2-degree of freedom (DOF) model is designed. In addition, considering the inaccuracy of the vehicle model, a nonlinear model predictive control is designed based on the 7-DOF model. For the lower controller, a torque allocation algorithm considering stability and economy is proposed, which ensures the stability of the vehicle and reduces the energy consumption of the powertrain. Moreover, the weight distribution of stability and economy is dynamically coordinated based on the phase portrait. Finally, the algorithm is verified under the conditions of low adhesion road, joint road, driving cycle and hardware-in-the-loop test. The results show that the proposed torque vectoring algorithm can ensure the vehicle handling stability and improve the energy efficiency of the powertrain at the same time.

Suggested Citation

  • Deng, Huifan & Zhao, Youqun & Feng, Shilin & Wang, Qiuwei & Zhang, Chenxi & Lin, Fen, 2021. "Torque vectoring algorithm based on mechanical elastic electric wheels with consideration of the stability and economy," Energy, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s036054422032750x
    DOI: 10.1016/j.energy.2020.119643
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    References listed on IDEAS

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    1. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2019. "Optimal sizing and adaptive energy management of a novel four-wheel-drive hybrid powertrain," Energy, Elsevier, vol. 187(C).
    2. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    3. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    4. Elshkaki, Ayman, 2020. "Long-term analysis of critical materials in future vehicles electrification in China and their national and global implications," Energy, Elsevier, vol. 202(C).
    5. Han, Zhongliang & Xu, Nan & Chen, Hong & Huang, Yanjun & Zhao, Bin, 2018. "Energy-efficient control of electric vehicles based on linear quadratic regulator and phase plane analysis," Applied Energy, Elsevier, vol. 213(C), pages 639-657.
    6. Hu, Xiao & Wang, Ping & Hu, Yunfeng & Chen, Hong, 2020. "A stability-guaranteed and energy-conserving torque distribution strategy for electric vehicles under extreme conditions," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. Xu, Tao & Zhao, Youqun & Deng, Huifan & Guo, Shuo & Li, Danyang & Lin, Fen, 2023. "Integrated optimal control of distributed in-wheel motor drive electric vehicle in consideration of the stability and economy," Energy, Elsevier, vol. 282(C).
    2. Lipeng, Zhang & Xin, Liu & Shuaishuai, Liu & Haoran, Guo & Kaixin, Shi, 2024. "Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps," Energy, Elsevier, vol. 289(C).
    3. Wei, Hongqian & Ai, Qiang & Zhao, Wenqiang & Zhang, Youtong, 2022. "Modelling and experimental validation of an EV torque distribution strategy towards active safety and energy efficiency," Energy, Elsevier, vol. 239(PA).

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