Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning

被引:24
|
作者
Na, Gyujin [1 ]
Park, Gyunghoon [2 ]
Turri, Valerio [3 ,4 ]
Johansson, Karl H. [3 ,4 ]
Shim, Hyungbo [5 ]
Eun, Yongsoon [1 ]
机构
[1] DGIST, Dept Informat & Commun Engn, Daegu, South Korea
[2] Korea Inst Sci & Technol, Ctr Intelligent & Interact Robot, Seoul, South Korea
[3] KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden
[4] KTH Royal Inst Technol, Dept Automat Control, Stockholm, Sweden
[5] Seoul Natl Univ, Dept Elect & Comp Engn, ASRI, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Heavy-duty vehicle platoon; road slope estimation; robust control; disturbance observer; ROAD-SLOPE ESTIMATION; LOOK-AHEAD CONTROL; STABILITY; SAFETY; TIME;
D O I
10.1080/00423114.2019.1704803
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Heavy-duty vehicle platooning has received much attention as a method to reduce fuel consumption by keeping inter-vehicle distance short. When a platoon follows a fuel-optimal velocity profile calculated using preview road slope information, significant improvement in the fuel economy occurs. To calculate the optimal velocity in the existing method, however, platoon should acquire expensive road slope data in advance. As an alternative, we propose a road slope estimation method, which enables platoon to calculate the optimal velocity profile without the usage of actual road slope data. Other major challenges in platoon operation include overcoming the effect of the vehicle model uncertainties and external disturbances for ensuring the control performance. The most significant part of the disturbances arises from slopes along a route. Existing method for reducing the effect of the slope employs a feed-forward type compensation in the control loop by combining the vehicle position acquired from GPS and the slope database. However, this method exhibits limitations: the mass of the vehicles in the platoon is uncertain which lowers the accuracy of the feed-forward compensation, and the platoon requires the pre-acquired slope database. To overcome these limitations, we propose an alternative method employing disturbance observer. Simulations of various scenarios are conducted to show the efficacy of the proposed method using the actual road slope data of a Swedish highway.
引用
收藏
页码:748 / 767
页数:20
相关论文
共 50 条
  • [1] Gear management for fuel-efficient heavy-duty vehicle platooning
    Turri, Valerio
    Besselink, Bart
    Johansson, Karl H.
    [J]. 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), 2016, : 1687 - 1694
  • [2] Fuel-efficient control of merging maneuvers for heavy-duty vehicle platooning
    Koller, J. P. J.
    Colin, A. Grossmann
    Besselink, B.
    Johansson, K. H.
    [J]. 2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, : 1702 - 1707
  • [3] Fuel-efficient heavy-duty vehicle platooning by look-ahead control
    Turri, Valerio
    Besselink, Bart
    Martensson, Jonas
    Johansson, Karl H.
    [J]. 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, : 654 - 660
  • [4] Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning
    Turri, Valerio
    Besselink, Bart
    Johansson, Karl H.
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2017, 25 (01) : 12 - 28
  • [5] Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization
    [J]. Torabi, Sina (sina.torabi@chalmers.se), 1600, Hindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States (2018):
  • [6] Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization
    Torabi, Sina
    Wahde, Mattias
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2018,
  • [7] Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks
    Ling, Gustav
    Lindsten, Klas
    Ljungqvist, Oskar
    Lofberg, Johan
    Noren, Christoffer
    Larsson, Christian A.
    [J]. 2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 3994 - 4001
  • [8] The Fuel-Efficient Platooning of Heavy Duty Vehicles by Mathematical Programming and Genetic Algorithm
    Nourmohammadzadeh, Abtin
    Hartmann, Sven
    [J]. THEORY AND PRACTICE OF NATURAL COMPUTING, TPNC 2016, 2016, 10071 : 46 - 57
  • [9] Vehicle-to-Vehicle Communication for Safe and Fuel-Efficient Platooning
    Sidorenko, Galina
    Thunberg, Johan
    Sjoberg, Katrin
    Vinel, Alexey
    [J]. 2020 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2020, : 795 - 802
  • [10] A Distributed Framework for Coordinated Heavy-Duty Vehicle Platooning
    Larson, Jeffrey
    Liang, Kuo-Yun
    Johansoon, Karl H.
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (01) : 419 - 429