Volume 21 Issue 2
Aug.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(2): 129-137
LI Yi-nong, ZHU Zhe-wei, ZHENG Ling, HU Yi-ming. Multi-objective control and optimization of active energy-regenerative suspension based on road recognition[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 129-137. doi: 10.19818/j.cnki.1671-1637.2021.02.011
Citation: LI Yi-nong, ZHU Zhe-wei, ZHENG Ling, HU Yi-ming. Multi-objective control and optimization of active energy-regenerative suspension based on road recognition[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 129-137. doi: 10.19818/j.cnki.1671-1637.2021.02.011
shu

Multi-objective control and optimization of active energy-regenerative suspension based on road recognition

doi: 10.19818/j.cnki.1671-1637.2021.02.011

  • College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China

Funds:

National Key Research and Development Program of China 2017YFB0102603-3

Science and Technology Project of Chongqing CSTC2018JCYJAX0630

More Information
  • Author Bio:

    LI Yi-nong(1961-), male, professor, PhD, ynli@cqu.edu.cn

  • Received Date: 2020-11-01
  • Publish Date: 2021-04-01
    Abstract
  • For the problem that the vibration reduction performance and energy-regenerative characteristics of active suspension are less adaptable under different road classes, a nonlinear electromagnetic active suspension model was constructed. Considering the suspension sprung mass uncertainty during vehicle driving, an adaptive sliding mode controller of active suspension was proposed. An adaptive fuzzy neural network and the dynamics data of suspension under different roads were used to recognize road classes and determine the objective coefficient of the controller. Then, the coordination between safety and comfort of active suspension was realized. The energy-regeneration characteristics and switch control strategies of electromagnetic active suspension were studied. On this basis, the suspension dynamic performance and energy-regeneration characteristic were taken as the design objectives, and the contradictory relationships between the safety, comfort, and energy efficiency of electromagnetic active suspension were considered to comprehensively optimize the controller and suspension structure parameters through the multi-objective particle swarm optimization (MOPSO). The optimal solution was acquired from the Pareto solution set after the multi-objective optimization according to the fuzzy set theory. Research result reveals that the fuzzy neural network gives a maximum recognition error within 10% for various road classes when the nonlinear electromagnetic active suspension is employed. Thus, it meets the requirement of recognition accuracy. For C-class roads, the vibration acceleration of sprung mass of optimized active suspension reduces by 35.3% compared with the traditional passive suspension. The tire dynamic displacement increases by 7.7%, but it is still within 10%, ensuring safety. Compared with the original active suspension, the optimized suspension has 10.5% less sprung mass vibration acceleration and 1.7% higher energy-regeneration efficiency. The optimized adaptive sliding mode controller can better balance the energy-regeneration and vibration reduction characteristics of suspension. The established nonlinear electromagnetic active suspension model can realize the comprehensive optimization of safety, comfort, and energy efficiency of suspension system under different road classes. 5 tabs, 9 figs, 30 refs.

     

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