Energy-optimal adaptive cruise control combining model predictive control and dynamic programming. (March 2018)

• Record Type: Journal Article
• Authors: Weißmann, Andreas; Görges, Daniel; Lin, Xiaohai
• Is Part Of: Control engineering practice. Volume 72(2018)
• Page Start: 125
• Page End: 137
• Publication Date: 2018-03
• Subjects: Adaptive cruise control -- Dynamic programming -- Model predictive control -- Cloud -- Prediction; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2017.12.001
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In this paper a novel approach for energy-optimal adaptive cruise control (ACC) combining model predictive control (MPC) and dynamic programming (DP) is presented. The approach uses knowledge about a given route to precalculate a position-dependent energy-optimal speed trajectory using DP while taking information like speed limits, road slope, and travel time into account during the optimization. A simple MPC framework is used to control the traction force of the host vehicle such that the vehicle speed follows the energy-optimal speed trajectory as good as possible while ensuring safety-related constraints like distance to a preceding vehicle or speed limits. To show the benefits of the approach, a comparison of the energy consumption between the host vehicle and the preceding vehicle on the same route is performed. For the speed profile of the preceding vehicle, data from real test drives is used. Simulations show that the approach leads to a significant reduction of the energy consumption compared to the preceding vehicle on the same route. Furthermore, the simulations indicate that the approach achieves high energy savings even with a poor prediction model for the preceding car. Moreover, the approach has shown to run very fast, indicating its real-time capability. Highlights: A novel approach for energy-optimal adaptive cruise control is presented. Dynamic programming is used for computing an energy-optimal speed trajectory. Model predictive control is used to track the energy-optimal speed trajectory. Model predictive control is furthermore used to maintain the safety distance. Simulations results indicate that the approach leads to significant energy savings.