Fuel-efficient heavy-duty vehicle platooning by look-ahead control

Turri, Valerio; Besselink, Bart; Mårtensson, Jonas; Johansson, Karl Henrik

doi:10.1109/cdc.2014.7039456

Cited by 46 publications

(32 citation statements)

References 14 publications

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“…However, it is clear that follower vehicles might be required to have a higher velocity on this hill (i.e., at the same location in space) when they are subject to a constant spacing or constant headway policy. This might be infeasible due to limited engine power and leads to undesired platoon behavior, as recognized in Alam et al (2013) and Turri et al (2014).…”

Section: Spacing Policies and Motivationmentioning

confidence: 97%

Control of platoons of heavy-duty vehicles using a delay-based spacing policy∗∗This research is funded by the FP7 Programme of the European Union through the project COMPANION.

Besselink

Johansson

2015

IFAC-PapersOnLine

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The formation of groups of heavy-duty vehicles driving at close inter-vehicular distances (known as a platoon) reduces the fuel consumption due to a decreased aerodynamic drag and has the potential to increase traffic flow. This paper motivates the use of a novel spacing policy, which specifies the desired distance between vehicles as a function of their states. Particularly, a delay-based spacing policy is introduced, which guarantees that all vehicles in the group follow the same velocity profile in space. It is shown that the proposed spacing policy offers advantages in the platoon control for non-constant reference velocities, which are common when driving over hilly terrain. A controller is designed to achieve this spacing policy, hereby exploiting an analysis of the vehicle dynamics in the spatial domain rather than time domain. Moreover, the controller is shown to attenuate the effect of disturbances as they propagate through the platoon. Simulations are used to illustrate the effectiveness of the proposed spacing policy and controller.

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Section: Spacing Policies and Motivationmentioning

confidence: 97%

Control of platoons of heavy-duty vehicles using a delay-based spacing policy∗∗This research is funded by the FP7 Programme of the European Union through the project COMPANION.

Besselink

Johansson

2015

IFAC-PapersOnLine

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“…Examples of such control strategies are given by [5], [8], [12], whereas other works focus on topics ranging from the influence of the inter-vehicular spacing policy [14] or road topography [15] to stability properties [9]. These works, however, have in common that they assume that the vehicles are already in a platoon.…”

Section: Introductionmentioning

confidence: 97%

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

Koller

Colin

Besselink

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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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

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“…Also F i ext represents the known disturbance on the vehicle that summarizes the forces from gravity, roll resistance and aerodynamic resistance, (Turri et al, 2014). Here, we assume that this disturbance is constant throughout the prediction horizon.…”

Section: Dynamic Modelmentioning

confidence: 99%

“…The last set of constraints in the problem assures that each vehicle in the platoon stays a safe distance away from the neighboring vehicles, and in the worst case can brake and stop without colliding with the front vehicle. This constraint for each vehicle, i, can be written as (Turri et al, 2014). This constraint is denoted as q s (x i (t d )) ≤ 0.…”

Section: Constraints Defining the Feasible Operating Areamentioning

confidence: 99%

“…Here, a platoon refers to a group of vehicles that are moving at close inter-vehicular distances, commonly, in a chain. The platooning problem then corresponds to the problem of devising control strategies that enable the vehicles to safely operate at such close distances so that the platoon moves with a speed that is close to a given reference value, see e.g., Turri et al (2014); Dunbar and Caveney (2012); di Bernardo et al (2015); Dold and Stursberg (2009). Here we use model predictive control (MPC) for solving this control problem.…”

Section: Distributed Predictive Control Of Platoons Of Vehiclesmentioning

confidence: 99%

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Divide and Conquer: Distributed Optimization and Robustness Analysis

Pakazad

2015

Linköping Studies in Science and Technology. Dissertations

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Linköping 2015Cover illustration: A design approach for distributed optimization alogirhtms, used in papers C and F. It states that given the sparstiy graph of a coupled optimization problem, defined in Section 5.1, we can group its variables and its constituent subprobelms so that the coupling structure can be represented using a tree. We can then solve the problem distributedly using message passing, which utilizes the tree as its computational graph. To my parents AbstractAs control of large-scale complex systems has become more and more prevalent within control, so has the need for analyzing such controlled systems. This is particularly due to the fact that many of the control design approaches tend to neglect intricacies in such systems, e.g., uncertainties, time delays, nonlinearities, so as to simplify the design procedure. Robustness analysis techniques allow us to assess the effect of such neglected intricacies on performance and stability. Performing robustness analysis commonly requires solving an optimization problem. However, the number of variables of this optimization problem, and hence the computational time, scales badly with the dimension of the system. This limits our ability to analyze large-scale complex systems in a centralized manner. In addition, certain structural constraints, such as privacy requirements or geographical separation, can prevent us from even forming the analysis problem in a centralized manner.In this thesis, we address these issues by exploiting structures that are common in large-scale systems and/or their corresponding analysis problems. This enables us to reduce the computational cost of solving these problems both in a centralized and distributed manner. In order to facilitate distributed solutions, we employ or design tailored distributed optimization techniques. Particularly, we propose three distributed optimization algorithms for solving the analysis problem, which provide superior convergence and/or computational properties over existing algorithms. Furthermore, these algorithms can also be used for solving general loosely coupled optimization problems that appear in a variety of fields ranging from control, estimation and communication systems to supply chain management and economics.v Populärvetenskaplig sammanfattningRegulatorer för styrning av system är ofta designade med hjälp av en beskrivning, typiskt en matematisk modell, av det man vill styra. Dessa modeller är oftast endast approximativa beskrivningar av det verkliga systemet och därför behäftade med osäkerheter. Detta medför att det beteende man förväntar sig av ett reglerat system baserat på den matematiska modellen inte alltid stämmer överens med det observerade beteendet. Att analysera hur stor denna avvikelse kan bli kallas robusthetsanalys. Detta är ett väl studerat område för mindre och medelstora system. Dock är kunskaperna begränsade för riktigt stora system, speciellt om man inte vill göra en alltför konservativ analys. Exempel på stora system är kraftnät och flygplan. Kraftnät är stora på gr...

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Fuel-efficient heavy-duty vehicle platooning by look-ahead control

Cited by 46 publications

References 14 publications

Control of platoons of heavy-duty vehicles using a delay-based spacing policy∗∗This research is funded by the FP7 Programme of the European Union through the project COMPANION.

Control of platoons of heavy-duty vehicles using a delay-based spacing policy∗∗This research is funded by the FP7 Programme of the European Union through the project COMPANION.

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

Divide and Conquer: Distributed Optimization and Robustness Analysis

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