Fuel-Efficient Control of Merging Maneuvers for Heavy-Duty Vehicle Platooning

Abstract

With the rapid increase in the research of promising technologies such as vehicle platooning, which are trying to find more fuel-efficient ways of operating our transportation systems, complex questions requiring creative solutions arise. Regarded as an up-and-coming technology that is expected to be highly relevant and widely applied in the near future, vehicle platooning allows the smart manipulation of the traffic by grouping two or more vehicles into a single convoy and coordinating its behavior, all the while guaranteeing a safe and fuel-efficient freight operation. This thesis tackles the specific problem in which two platoons of heavyduty vehicles (HDVs), coming from different highways, would like to be merged into a single one and remain driving as a larger platoon using the least amount of fuel possible. This can be beneficial if the HDVs need to travel a long distance after the merging point and fuel savings of up to 8% can be achieved by platooning along this trajectory. The focus lies on the calculation of the optimal input policies that minimize the fuel consumption of both platoons while completing the merging maneuver. Some theoretical background on optimal control and the minimum principle is introduced and it is consequently applied on a nonlinear model of the HDVs dynamics. The culmination of this approach is the solution of the inputconstrained, free end-time optimization problem. Due to the non-linearity of the model and the conditions set by the minimum principle numerical integration techniques, such as the shooting method, are also presented. In a final stage, this optimization tool is used together with shrinking horizon model predictive control to create a controller that is capable of accounting for disturbances, speed constraints and model uncertainties. The final result of this thesis is a control algorithm that can merge two platoons of any length, even in the presence of undesirable disturbances by correcting the optimal policies accordingly, in the most fuel-efficient manner

Cyber–Physical Control of Road Freight Transport

“…Moreover, a receding horizon implementation of the optimal merging procedure can be used to guarantee robustness with respect to disturbances such as the influence of surrounding traffic. These extensions can be found in [30].…”

“…[48], as detailed in [30]. In the first step, after selecting a fixed merging time t m , the problem reduces to the fuel-optimal traversal of a given road segment.…”

Control of connected and automated vehicles: State of the art and future challenges

“…[127] presents a hierarchical control architecture for freight transportation, including platoon merging, splitting or reordering. Speed trajectories to merge into a growing platoon are computed in [207] using an optimal control approach. The algorithm receives the origins, destinations, and times of departure and arrival of the lead platoon and of the vehicles joining along the road; using a hybrid systems extension of Pontryagin's minimum principle, it computes the optimal merging times and the corresponding velocity trajectories.…”

Splitting and merging control of multiple platoons with Signal Temporal Logic

“…In the majority of the works, consensus protocols are designed for longitudinal control under limited inter-vehicle communication as in [1]- [3] and stability is established often under communication delays and/or varying communication topologies. In [4]- [7] the splitting and/or merging problem of single vehicles was studied and distributed or centralized control schemes were proposed for the design of safe trajectories ensuring these tasks. A detailed review on merging control strategies for connected and automated vehicles (CAVs) can be found in [8].…”