An iterative optimization-based predictive control method for eco-driving of unmanned vehicles

doi:10.3969/j.issn.1674-8484.2023.01.010

Abstract

Abstract:

A data-driven Iterative Optimization-Based Predictive Control (IOBPC) method for energy saving of unmanned heavy vehicles was proposed. Based on the historical data, the terminal state constraint set and terminal cost function were constructed and updated iteratively. And the approximate treatment of the constraint set and terminal cost function improved the computational efficiency of the algorithm. By learning the correlation between vehicle state trajectory and fuel consumption, the cost function of the optimization algorithm was guaranteed to decrease monotonically and converge in the iterative process, so as to realize the significant improvement of vehicle fuel economy. The results show that the iterative optimization predictive controller makes the vehicle trajectory converged and reduces fuel consumption by about 10.2% after several iterations. Compared with the energy-saving driving strategy based on dynamic programming (DP), the energy-saving effect is further improved. Moreover, it has fewer adjustment parameters and supports real-time solution, which is more conducive to practical application.

Key words: unmanned heavy vehicles, eco-driving, iterative optimization, predictive control

CLC Number:

U463.61

LIU Yi, GONG Xinle, TANG Yun, HU Man, MA Jie, QIN Yi, WU Fei, PU Huayan, LUO Jun. An iterative optimization-based predictive control method for eco-driving of unmanned vehicles[J]. Journal of Automotive Safety and Energy, 2023, 14(1): 80-88.

Figures/Tables 11

References 24

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整车质量，m	30 t
滚动阻力因数，C_r	0.005 5
空气阻力因数，C_D	0.69
车辆横截面积，A	10 m²
车身长度，l	8 m

整车质量，m	30 t
滚动阻力因数，C_r	0.005 5
空气阻力因数，C_D	0.69
车辆横截面积，A	10 m²
车身长度，l	8 m

离散时间，t_s	0.1 s
预测步长，N	30
约束集步长，N_f	10

离散时间，t_s	0.1 s
预测步长，N	30
约束集步长，N_f	10

离散时间，t_s	0.2 s
预测步长， N	30
约束集步长， N_f	10