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Model Predictive Controller-Based Optimal Slip Ratio Control System for Distributed Driver Electric Vehicle

Author

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  • Qingxian Li
  • Liangjiang Liu
  • Xiaofang Yuan

Abstract

The slip ratio control is an important research topic in in-wheel-motored electric vehicles (EVs). Traditional control methods are usually designed for some specified modes. Therefore, the optimal slip ratio control cannot be achieved while vehicles work under various modes. In order to achieve the optimal slip ratio control, a novel model predictive controller-based optimal slip ratio control system (MPC-OSRCS) is proposed. The MPC-OSRCS includes three parts, a road surface adhesion coefficient identifier, an operation mode recognizer, and an MPC based-optimal slip ratio control. The current working road surface is identified by the road surface adhesion coefficient identifier, and a modified recursive Bayes theorem is used to compute the matching degree between current road surfaces and reference road surfaces. The current operation state is recognized by the operation mode recognizer, and a fuzzy logic method is applied to compute the matching degree between actual operation state and reference operation modes. Then, a parallel chaos optimization algorithm (PCOA)-based MPC is used to achieve the optimal control under various operation modes and different road surfaces. The MPC-OSRCS for EV is verified on simulation platform and simulation results under various conditions to show the significant performance.

Suggested Citation

  • Qingxian Li & Liangjiang Liu & Xiaofang Yuan, 2020. "Model Predictive Controller-Based Optimal Slip Ratio Control System for Distributed Driver Electric Vehicle," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, April.
    • Handle: RePEc:hin:jnlmpe:8086590
    DOI: 10.1155/2020/8086590
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    Cited by:

    1. Idris Idris Sunusi & Jun Zhou & Chenyang Sun & Zhenzhen Wang & Jianlei Zhao & Yongshuan Wu, 2021. "Development of Online Adaptive Traction Control for Electric Robotic Tractors," Energies, MDPI, vol. 14(12), pages 1-24, June.

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