交通货运的网络物理控制
Cyber-physical Control of Road Freight Transport
AI总结：
本文探讨如何利用现代信息技术实现网络物理运输系统架构，通过协调卡车车队实现节能降耗，展示协同控制策略在货运中的应用与效果。
摘要：
货运运输对社会至关重要且持续增长。同时，道路运输约占欧盟所有能源消耗的26%和温室气体排放的18%。尽管运输系统对能源消耗和环境有影响，但道路运输主要由个体长途卡车完成，缺乏实时协调或全局优化。本文回顾了现代信息技术如何支持网络物理运输系统架构，实现集成物流系统协调行驶的卡车车队。通过减少空气阻力，行驶紧密的车队可节省约10%的燃料消耗。利用道路等级信息和车辆间通信，在现有巡航控制器基础上实施安全且燃料优化的协同前瞻性控制策略。通过优化车辆与车队的交互，可实现显著改进。集成的运输规划和车辆路线规划在车队管理系统中允许大小车队所有者受益于协作。描述了一个包含200辆重型卡车在瑞典执行运输任务的真实案例研究。模拟显示，由于协调的车队规划，总体燃油节省超过5%。还展示了所提出的协同前瞻性控制器如何有效优化车辆在起伏路段的速度剖面。
英文摘要：
Freight transportation is of outmost importance for our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of all energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.
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