Journal
IFAC PAPERSONLINE
Volume 50, Issue 1, Pages 4794-4799Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ifacol.2017.08.963
Keywords
Adaptive cruise control; model predictive control; electric vehicles; energy efficiency; modeling for control optimization
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Funding
- Center for Commercial Vehicle Technology (ZNT) at the University of Kaiserslautern - Research Initiative of the Federal State of Rhineland-Palatinate
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This paper presents a simplified approach for energy-efficient adaptive cruise control based on model predictive control (MPC). The goal of the approach is to reduce the energy consumption of a controlled vehicle by using MPC to smoothen the velocity profile such that the acceleration and deceleration are minimized considering available environment information. In the vehicle following scenario, the controlled vehicle is allowed to move in an inter-vehicle distance corridor bounded by a safe minimal distance and a maximal distance. Thereby aspects of road safety and traffic efficiency are addressed. Nonlinear system limitations are approximated with linear constraints. As a result, a quadratic programming problem with linear constraints is formulated, which can be solved using standard methods. A simulation study using velocity profiles for the leading vehicle from real trips and therefore capturing realistic traffic situations is presented. The energy consumption of the controlled and the leading vehicle is evaluated using an electric vehicle model from the literature. Comparisons between the controlled vehicle and the leading vehicle indicate fair energy savings. Furthermore, the computational complexity of the optimization strategy is investigated. A reasonable compromise between real-time capability and energy consumption reduction is obtained. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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