Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle

被引:39
|
作者
Zeng, Xiaohua [1 ]
Li, Guanghan [1 ]
Yin, Guodong [2 ]
Song, Dafeng [1 ]
Li, Sheng [3 ]
Yang, Nannan [1 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130025, Jilin, Peoples R China
[2] Southeast Univ, Sch Mech Engn, Nanjing 210096, Jiangsu, Peoples R China
[3] FAW Jiefang Automot CO LTD, Qingdao 266043, Peoples R China
基金
中国国家自然科学基金;
关键词
Hydraulic hub-motor auxiliary system; Dynamic coordinate; Model predictive control; Nonlinear model; Heavy commercial vehicle; REGENERATIVE BRAKING; CONTROL ALLOCATION; PNEUMATIC TIRES; ELECTRIC VEHICLES; SIMULATIONS; FEEDFORWARD;
D O I
10.1016/j.ymssp.2017.08.029
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Equipping a hydraulic hub-motor auxiliary system (HHMAS), which mainly consists of a hydraulic variable pump, a hydraulic hub-motor, a hydraulic valve block and hydraulic accumulators, with part-time all-wheel-drive functions improves the power performance and fuel economy of heavy commercial vehicles. The coordinated control problem that occurs when HHMAS operates in the auxiliary drive mode is addressed in this paper; the solution to this problem is the key to the maximization of HHMAS. To achieve a reasonable distribution of the engine power between mechanical and hydraulic paths, a nonlinear control scheme based on model predictive control (MPC) is investigated. First, a nonlinear model of HHMAS with vehicle dynamics and tire slip characteristics is built, and a controller-design-oriented model is simplified. Then, a steady-state feedforward + dynamic MPC feedback controller (FMPC) is designed to calculate the control input sequence of engine torque and hydraulic variable pump displacement. Finally, the controller is tested in the MATLAB/Simulink and AMESim co-simulation platform and the hardware-in-the loop experiment platform, and its performance is compared with that of the existing pro portional-integral-derivative controller and the feedforward controller under the same conditions. Simulation results show that the designed FMPC has the best performance, and control performance can be guaranteed in a real-time environment. Compared with the tracking control error of the feedforward controller, that of the designed FMPC is decreased by 85% and the traction efficiency performance is improved by 23% under a low-friction-surface condition. Moreover, under common road conditions for heavy commercial vehicles, the traction force can increase up to 13.4-15.6%. (C) 2017 Elsevier Ltd. All rights reserved.
引用
下载
收藏
页码:97 / 120
页数:24
相关论文
共 50 条
  • [41] Model predictive control-based cooperative lane change strategy for improving traffic flow
    Wang, Di
    Hu, Manjiang
    Wang, Yunpeng
    Wang, Jianqiang
    Qin, Hongmao
    Bian, Yougang
    ADVANCES IN MECHANICAL ENGINEERING, 2016, 8 (02) : 1 - 17
  • [42] Low Frequency Ratio Deadbeat Predictive Torque Control of Permanent Magnet Synchronous Motor in Auxiliary Coordinate System
    Yan Y.
    Zhao M.
    Chen Z.
    Li M.
    Shi T.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2023, 43 (02): : 761 - 769
  • [43] Neural Network-Based Model Predictive Control of a Servo-Hydraulic Vehicle Suspension System
    Dahunsi, O. A.
    Pedro, J. O.
    Nyandoro, O. T.
    2009 AFRICON, VOLS 1 AND 2, 2009, : 742 - 747
  • [44] Guaranteed Cost Model Predictive Control-based Driver Assistance System for Vehicle Stabilization Under Tire Parameters Uncertainties
    Massera, Carlos M.
    Terra, Marco H.
    Wolf, Denis F.
    2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2016, : 322 - 327
  • [45] Model Predictive Control-based Drive Assist Control in Electric Vehicle -An Application to Inter Distance Control Considering Human Model
    Okuyama, Yuji
    Murakami, Toshiyuki
    MECATRONICS REM 2012, 2012, : 153 - 160
  • [46] A Robust Model Predictive Control-Based Scheduling Approach for Electric Vehicle Charging With Photovoltaic Systems
    Yang, Yu
    Yeh, Hen-Geul
    Nguyen, Richard
    IEEE SYSTEMS JOURNAL, 2023, 17 (01): : 111 - 121
  • [47] Model Predictive Control-Based Controller Design for a Power-Split Hybrid Electric Vehicle
    Wang, Weida
    Jia, Shipeng
    Xiang, Changle
    Huang, Kun
    Zhao, Yulong
    2014 PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION & CONTROL (ICMIC), 2014, : 219 - 224
  • [48] Extended Kalman filter-based and model predictive control-based dynamic coordinated control strategy for power-split hybrid electric bus
    Zeng, Xiaohua
    Liu, Tong
    Song, Dafeng
    Yang, Nannan
    Cui, Haoyong
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2020, 234 (06) : 1623 - 1633
  • [49] Model-based braking control of a heavy commercial road vehicle equipped with an electropneumatic brake system
    Gautam, Vikas
    Rajaram, Vignesh
    Subramanian, Shankar C.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2017, 231 (12) : 1693 - 1708
  • [50] Research and Bench Test of Nonlinear Model Predictive Control-Based Power Allocation Strategy for Hybrid Energy Storage System
    Zhao Yulong
    Wang Weida
    Xiang Changle
    Liu Hui
    Langari, Reza
    IEEE ACCESS, 2018, 6 : 70770 - 70787