Coordination of Heavy-Duty Vehicle Platooning

Sammanfattning: A network-wide coordination system for heavy-duty vehicle platooning with the purpose of reducing fuel consumption is developed. Road freight is by far the dominating mode for overland transport with over 60 % modal share in the OECD countries and is thus critically important for the economy. Overcoming its strong dependency on fossil fuels and manual labor as well as handling rising congestion levels are therefore important societal challenges. Heavy-duty vehicle platooning is a promising near-term automated-driving technology. It combines vehicle-to-vehicle communication and on-board automation to slipstream in a safe manner, which can reduce fuel consumption by more than 10 %. However, in order to realize these benefits in practice, a strategy is needed to form platoons in an operational context. We propose a platoon coordination system that supports the process of automatically forming platoons over large geographic areas.We develop an architecture in which fleet management systems send start locations, destinations, and arrival deadlines to a platoon coordinator. By computing desirable speed profiles and by letting the vehicles' on-board systems track them, vehicles can meet en route and form platoons. Matching vehicles into platoons and deriving suitable speed profiles is treated as an optimization problem with the objective of maximizing the overall fuel savings under the constraint that vehicles arrive in time at their destinations. By updating the speed profiles and the platoon configurations based on real-time measurements of vehicle position and platoon state, the system can accommodate new vehicles joining on the fly. Using real-time measurements also makes the system resilient to disturbances and changing operating conditions. This thesis seeks to develop the theoretical foundations of such a system and evaluate its potential to improve transport efficiency.  We first explore the coordination of vehicle pairs. Fuel-optimal speed profiles are derived. The uncertainty arising from traffic is taken into account by modeling travel time distributions and considering the probability of two vehicles successfully merging. Building on this coordination algorithm for vehicle pairs, we derive algorithms for larger platoons and vehicle fleets. This results in an NP-hard combinatorial optimization problem. The problem is formulated as an integer program and results on the solution structure are derived. In order to handle realistic fleet sizes with thousands of vehicles and continental sized geographical areas under real-time operation, heuristic algorithms are developed. The speed profiles resulting from the combinatorial optimization are further improved using convex optimization. Moreover, we derive efficient algorithms to identify all pairs of vehicles that can platoon. Simulations demonstrate that the proposed algorithm is able to compute plans for thousands of vehicles. Coordinating approximately a tenth of Germany's heavy-duty vehicle traffic, platooning rates over 65 % can be achieved and fuel consumption can be reduced by over 5 %. The proposed system was implemented in a demonstrator system. This demonstrator system has been used in experiments on public roads that show the technical feasibility of en route platoon coordination.