Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle emissions and performance · Traffic control and management · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan
Platform of Inter/Transdisciplinary Energy Research, Kyushu University, Fukuoka 819-0395, Japan
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