Predictive cruise control for heavy trucks based on slope information under cloud control system

作     者：LI Shuyan WAN Keke GAO Bolin LI Rui WANG Yue LI Keqiang LI Shuyan;WAN Keke;GAO Bolin;LI Rui;WANG Yue;LI Keqiang

作者机构：College of EngineeringChina Agricultural UniversityBeijing 100083China School of Vehicle and MobilityTsinghua UniversityBeijing 100084China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2022年第33卷第4期

页      面：812-826页

核心收录：

学科分类：08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 080201[工学-机械制造及其自动化] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key Research and Development Program (2021YFB2501003) the Key Research and Development Program of Guangdong Province (2019B090912001) the China Postdoctoral Science Foundation (2020M680531)。 

主　　题：predictive cruise control(PCC) cloud control system(CCS) layered architecture road point segmentation method(RPSM) economic driving 

摘      要：With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.