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Model-Predictive Optimization for Pure Electric Vehicle during a Vehicle-Following Process

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  • Sheng Zhang
  • Xiangtao Zhuan

Abstract

In this paper, a pure electric vehicle (PEV) equipped with adaptive cruise control (ACC) system is studied for a vehicle-following process. And a multiobjective optimization algorithm for ACC system is proposed in a model-predictive control (MPC) framework for optimizing safety, tracking capability, driving comfortability and energy consumption. The longitudinal dynamics of the ACC system are modeled, which not only considers the vehicle spacing and speed, but also introduces the acceleration and the change rate of acceleration (jerk) for the host vehicle and fully considers the influence of the acceleration of the leading vehicle. The improvement of driving comfortability and the reduction of energy consumption are achieved mainly by optimizing the acceleration and jerk of host vehicle. Some optimized reference trajectories are introduced to MPC for improving driving comfortability of host vehicle. The performances of the multiobjective upper level algorithm combined with the PEV model are evaluated for three representative scenarios. The results demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Sheng Zhang & Xiangtao Zhuan, 2019. "Model-Predictive Optimization for Pure Electric Vehicle during a Vehicle-Following Process," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, May.
    • Handle: RePEc:hin:jnlmpe:5219867
    DOI: 10.1155/2019/5219867
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    Cited by:

    1. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.

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