Citation

Xu ZH, Li JH, Xiao F, Zhang X, Song SX, Wang D, Qi CY, Wang JF, Peng SL. Int. J. Automot. Technol. 2022; 23(2): 439-450.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12239-022-0040-z

PMID

unavailable

Abstract

This study analyzes the problem of adaptive cruise control of vehicles in different driving cycles and divides diverse weight coefficient intervals for the vehicles under the different driving cycles to improve the adaptability of the vehicles in various environments. This paper first describes the driving environment of the adaptive cruise vehicle, and a model prediction algorithm with fixed weight coefficients is established to control the vehicle state. Then, a neural network is established to identify the vehicle driving cycles, the weight intervals are divided in accordance with different driving cycles, and the weight value is dynamically adjusted through fuzzy control. Lastly, the variable weight coefficients of different driving cycles are combined with the model prediction controller. The software cosimulation shows that the method designed in this paper plays a positive role in the fuel economy of adaptive cruise.

Language: en

Keywords

Adaptive cruise control; Driving cycles; Fuel economy; Model predictive control; Weight coefficient