Simplified Energy-Efficient Adaptive Cruise Control based on Model Predictive Control

摘要

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.