Literature Database Entry

jia2023performance

Yanzhao Jia, Anas Abdelkarim, Xiaonan Klingbeil, Rene Savelsberg and Daniel Görges, "Performance Evaluation of Energy-Optimal Adaptive Cruise Control in Simulation and on a Test Track," Elsevier IFAC-PapersOnLine, vol. 56 (2), pp. 4994–5000, 2023.

Abstract

This paper presents a comprehensive evaluation of energy-optimal adaptive cruise control (EACC) based on model predictive control (MPC) in comparison to standard adaptive cruise control (ACC) which is equipped for mass-produced cars of a German car maker. First, a simulation study is made in this work to compare the performance of PID-based control and MPC. Afterwards, a prototype of EACC is installed in an electric vehicle (EV), which is tested as the host car and follows a preceding car on a test track. In the test cycles, which represent urban and rural driving scenarios, EACC reduces the energy consumption of the host car by up to 13% in comparison with the standard ACC. The results of the vehicle tests not only demonstrate a significant energy reduction by EACC, but also show that EACC can reduce the risk of rear-end collisions by reacting earlier to rapid deceleration of the vehicle in front. Furthermore, the passengers feel more comfortable as the vehicle speed trajectory is smoothened by EACC. Finally, to investigate the real-time implementation capability of the optimization algorithms, this paper also examines the computation time of the MPC-based EACC function with different lengths of the MPC horizon.

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Yanzhao Jia
Anas Abdelkarim
Xiaonan Klingbeil
Rene Savelsberg
Daniel Görges

BibTeX reference

@article{jia2023performance,
    author = {Jia, Yanzhao and Abdelkarim, Anas and Klingbeil, Xiaonan and Savelsberg, Rene and G{\"{o}}rges, Daniel},
    doi = {10.1016/j.ifacol.2023.10.1276},
    title = {{Performance Evaluation of Energy-Optimal Adaptive Cruise Control in Simulation and on a Test Track}},
    pages = {4994--5000},
    journal = {Elsevier IFAC-PapersOnLine},
    issn = {2405-8963},
    publisher = {Elsevier},
    number = {2},
    volume = {56},
    year = {2023},
   }
   
   

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Last modified: 2024-04-28