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    Cloud-Based Platoon Predictive Cruise Control Considering Fuel-Efficient and Platoon Stability

    Source: Journal of Transportation Engineering, Part A: Systems:;2024:;Volume ( 150 ):;issue: 003::page 04023146-1
    Author:
    Zhou Wang
    ,
    Duanfeng Chu
    ,
    Bolin Gao
    ,
    Liang Wang
    ,
    Xiaobo Qu
    ,
    Keqiang Li
    DOI: 10.1061/JTEPBS.TEENG-7920
    Publisher: ASCE
    Abstract: This work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [−0.25, 0.30] (m/s) and the space error at [−0.13, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%. This research contributes to solving the problem of energy-efficient driving in vehicle platoons. Based on the cloud control system (CCS), cloud-based platoon predictive cruise control (CPPCC) is proposed, which is a layered structure. The upper layer is the platoon speed planning layer in the cloud and the lower layer is the platoon stability control layer. By adding cloud nodes and changing the structure of the existing platoon predictive cruise control (PPCC) and communication topology, CPPCC is able to achieve the goals of platoon economy and stability. Compared with PF-CC and PLF-CC, it is able to achieve fuel savings of more than 5.13% and efficiency improvements of 5.71% while ensuring stable platoon operation. Deploying the vehicle-side platoon stabilization controller in a commercial vehicle platoon can provide a solution to existing PPCC for energy saving and stability control. Combined with cloud-based speed planning, this enables commercial vehicle platoon PCC. Solving the problem of energy consumption of existing commercial vehicles and thus reducing environmental pollution from logistics transport.
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      Cloud-Based Platoon Predictive Cruise Control Considering Fuel-Efficient and Platoon Stability

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4296892
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorZhou Wang
    contributor authorDuanfeng Chu
    contributor authorBolin Gao
    contributor authorLiang Wang
    contributor authorXiaobo Qu
    contributor authorKeqiang Li
    date accessioned2024-04-27T22:32:24Z
    date available2024-04-27T22:32:24Z
    date issued2024/03/01
    identifier other10.1061-JTEPBS.TEENG-7920.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296892
    description abstractThis work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [−0.25, 0.30] (m/s) and the space error at [−0.13, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%. This research contributes to solving the problem of energy-efficient driving in vehicle platoons. Based on the cloud control system (CCS), cloud-based platoon predictive cruise control (CPPCC) is proposed, which is a layered structure. The upper layer is the platoon speed planning layer in the cloud and the lower layer is the platoon stability control layer. By adding cloud nodes and changing the structure of the existing platoon predictive cruise control (PPCC) and communication topology, CPPCC is able to achieve the goals of platoon economy and stability. Compared with PF-CC and PLF-CC, it is able to achieve fuel savings of more than 5.13% and efficiency improvements of 5.71% while ensuring stable platoon operation. Deploying the vehicle-side platoon stabilization controller in a commercial vehicle platoon can provide a solution to existing PPCC for energy saving and stability control. Combined with cloud-based speed planning, this enables commercial vehicle platoon PCC. Solving the problem of energy consumption of existing commercial vehicles and thus reducing environmental pollution from logistics transport.
    publisherASCE
    titleCloud-Based Platoon Predictive Cruise Control Considering Fuel-Efficient and Platoon Stability
    typeJournal Article
    journal volume150
    journal issue3
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-7920
    journal fristpage04023146-1
    journal lastpage04023146-15
    page15
    treeJournal of Transportation Engineering, Part A: Systems:;2024:;Volume ( 150 ):;issue: 003
    contenttypeFulltext
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