Abstract
An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system.
Highlights
Reducing fuel consumption, carbon dioxide (CO2 ), and other air pollution emission has been an impending sustainable problem facing the transportation sector, especially the automobile industry, which is currently stalemating over oil scarcity and environmental concerns
The simulation test bench is established to evaluate the performance of the proposed multiple objectives optimization strategy for the adaptive cruise control system
The model predictive control (MPC) controller regulates the velocity of the host vehicle, a strict safe distance constraint, which means that the MPC controller is capable of applying more while maintaining a strict safe distance constraint, which means that the MPC controller is capable of aggressive maneuvers when the environment changes suddenly
Summary
Carbon dioxide (CO2 ), and other air pollution emission has been an impending sustainable problem facing the transportation sector, especially the automobile industry, which is currently stalemating over oil scarcity and environmental concerns. Another work that fully responds to the road condition, upcoming traffic signals, and spacing between vehicles of the real driving scene can generate rough driving behavior advice, such as speed up or slow down, to the user [16] These eco-driving assistance approaches are more about giving driving guidance qualitatively, rather than providing quantitative velocity or acceleration data required for fuel economy assessment. A multi-objective coordinated optimization strategy for the ACC system was designed based on the MPC method, which includes tracking capability, driving comfortability, and minimizing fuel consumption [24,25]. ACC system, which the shortage of existing microscopic traffic modelswith in handling multiple traffic-related constraints Most importantly, it offers and a possible solution forThe reducing fuel consumption, to between realize eco-driving pales in considering comfort fuel economy.
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