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Author:

Fang, Z. (Fang, Z..) | Duan, J. (Duan, J..) | Zheng, B. (Zheng, B..)

Indexed by:

Scopus

Abstract:

For energy-saving and improving ride comfort, this paper presents a novel development of the longitudinal motion control of intelligent vehicles based on two hierarchies optimal methods. The upper method is the Radau pseudo-spectral method (RPM), the lower method is the model predictive control (MPC). The RPM is used for energy consumption optimization algorithm. The MPC is used for the longitudinal motion control method. The longitudinal motion model and energy consumption model are developed. Based on the preceding models, the optimal control problem of energy consumption optimization is established, in combination with the boundary conditions and path constraints. Using the RPM to solve the problem, the optimal vehicle speed trajectory is obtainded as desired input The longitudinal motion control is completed based on MPC. Simulation results show that, in the case of a pure electric vehicle and an actual planning path, automatic driving at optimal speed consumes less power energy than automatic running at constant speed, and verify the effectiveness of the strategy in the paper. © 2015 IEEE.

Keyword:

energy consumption optimization; hierarchies optimal method; intelligent vehicle; longitudinal Motion; MPC; Radau pseudo-spectral method

Author Community:

  • [ 1 ] [Fang, Z.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, BJUT, Beijing, 100124, China
  • [ 2 ] [Duan, J.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, BJUT, Beijing, 100124, China
  • [ 3 ] [Zheng, B.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, BJUT, Beijing, 100124, China

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Source :

Proceedings - 2015 Chinese Automation Congress, CAC 2015

Year: 2016

Page: 1092-1097

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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