Cooperative Look-Ahead Control of Vehicle Platoon for Maximizing Fuel Efficiency Under System Constraints
Author(s) -
Chunjie Zhai,
Fei Luo,
Yonggui Liu
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2848480
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicles into a platoon at a short inter-vehicular distance. Additionally, the eco-driving technology has the potential to further increase fuel efficiency by optimizing the speed trajectories of vehicles. However, little research has been done into the eco-driving of a vehicle platoon. This paper proposes a cooperative look-ahead control strategy for maximizing the fuel efficiency of vehicle platoon travelling on a road with varying slopes. In this paper, the aerodynamic drag, nonlinear engine fuel consumption model, discrete gear ratios, and three engine operating modes are considered in the control strategy; under system constraints, a cooperative optimal fuel consumption problem of vehicle platoon based on distributed model predictive control is formulated; to obtain the optimal solution of the formulated nonlinear optimization problem, after the minimum fuel consumption table and the corresponding optimal control variable tables are obtained, the nonlinear optimization problem with discrete control variables is transformed into a 0-1 binary mixed linear programming problem. Simulation results show that, compared with benchmarks, the proposed control strategy can significantly improve fuel efficiency under different passenger comfort requirements, allowed speed ranges, and predictive control horizons.
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