Fuel-efficient predictive cruise control using the explicit MPC method for commercial vehicles
Fuel-efficient predictive cruise control (FPCC) is of great significance in achieving fuel conservation. Model predictive control (MPC) serves as a promising method for the design of the FPCC controller. However, existing MPC-based FPCC controller on real vehicles remains challenging since MPC needs to find the optimal control law at each time step with limited computation time and resource. In this paper, the authors propose a learning-based explicit MPC method to learn the optimal policy of FPCC systems. The authors employ the neural network to approximate the policy, and transfer the online computation burden of the optimal control law to the offline policy training process. Simulations demonstrate that the method can effectively improve the real-time performance and be generalised to different road topologies without sacrificing fuel economy and travel efficiency.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14775360
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Supplemental Notes:
- Copyright © 2024 Inderscience Enterprises Ltd.
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Authors:
- Zhang, Fawang
- Duan, Jingliang
- Yin, Yuming
- Jiao, Chunxuan
- Xie, Genjin
- Zhang, Congsheng
- Li, Shengbo Eben
- Xin, Zhe
- Publication Date: 2024
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 22-41
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Serial:
- International Journal of Vehicle Design
- Volume: 96
- Issue Number: 1
- Publisher: Inderscience Enterprises Limited
- ISSN: 1477-5360
- Serial URL: http://www.inderscience.com/jhome.php?jcode=IJVD
Subject/Index Terms
- TRT Terms: Autonomous intelligent cruise control; Commercial vehicles; Ecodriving; Fuel conservation; Machine learning
- Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01930609
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 16 2024 9:00AM