Energy-optimal trajectory planning for electric vehicles using Model Predictive Control

被引:1
作者
Rocha, Alexandre [1 ]
Ganesan, Anand [1 ,2 ]
Yang, Derong [2 ]
Murgovski, Nikolce [1 ]
机构
[1] Chalmers Univ Technol, Dept Elect Engn, Gothenburg, Sweden
[2] Volvo Cars, Dept Software Engn, R&D, Gothenburg, Sweden
来源
2024 EUROPEAN CONTROL CONFERENCE, ECC 2024 | 2024年
关键词
D O I
10.23919/ECC64448.2024.10590794
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a space-sampled Economic Model Predictive Control (EMPC) approach to jointly minimize total energy consumption of an electric vehicle (EV) and track both longitudinal velocity and path curvature reference trajectories. We consider a single-track vehicle model constrained to the range of accelerations +/- 3 m/s(2), and energy consumption is modelled explicitly including power losses of electric machines. Simulations with the high-fidelity simulator IPG CarMaker show the trade-off between energy consumption and reference tracking. Namely, results show how longitudinal velocity and acceleration control significantly impact energy consumption, whereas deviating from the path centerline mainly allows better velocity tracking.
引用
收藏
页码:1346 / 1351
页数:6
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