Title: Fuel-efficient predictive cruise control using the explicit MPC method for commercial vehicles

Authors: Fawang Zhang; Jingliang Duan; Yuming Yin; Chunxuan Jiao; Genjin Xie; Congsheng Zhang; Shengbo Eben Li; Zhe Xin

Addresses: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China; College of Engineering, China Agricultural University, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, Zhejiang University of Technology, Zhejiang, 310000, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' College of Engineering, China Agricultural University, Beijing, 100083, China ' College of Engineering, China Agricultural University, Beijing, 100083, China ' State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China ' College of Engineering, China Agricultural University, Beijing, 100083, China

Abstract: 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, we propose a learning-based explicit MPC method to learn the optimal policy of FPCC systems. We 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.

Keywords: commercial vehicles; reinforcement learning; predictive cruise control; eco-driving.

DOI: 10.1504/IJVD.2024.140432

International Journal of Vehicle Design, 2024 Vol.96 No.1, pp.22 - 41

Received: 11 Oct 2022
Accepted: 09 Oct 2023
Published online: 08 Aug 2024 *

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