Bart Besselink

Heavy-Duty Vehicle Platooning for Sustainable Freight Transportation: A Cooperative Method to Enhance Safety and Efficiency

The current system of global trade is largely based on transportation and communication technology from the 20th century. Advances in technology have led to an increasingly interconnected global market and reduced the costs of moving goods, people, and technology around the world. Transportation is crucial to society, and the demand for transportation is strongly linked to economic development. Specifically, road transportation is essential since about 60% of all surface freight transportation (which includes road and rail transport) is done on roads [2]. Despite the important role of road freight transportation in the economy, it is facing serious challenges, such as those posed by increasing fuel prices and the need to reduce greenhouse gas emissions. On the other hand, the integration of information and communication technologies to transportation systems-leading to intelligent transportation systems-enables the development of cooperative methods to enhance the safety and energy efficiency of transportation networks. This article focuses on one such cooperative approach, which is known as platooning. The formation of a group of heavy-duty vehicles (HDVs) at close intervehicular distances, known as a platoon (see Figure 1) increases the fuel efficiency of the group by reducing the overall air drag. The safe operation of such platoons requires the automatic control of the velocity of the platoon vehicles as well as their intervehicular distance.

Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning

The operation of groups of heavy-duty vehicles at a short inter-vehicular distance, known as platoon, allows one to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of trucks, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Maintaining a short inter-vehicular distance, as required by platooning, without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper, we propose a two-layer control architecture for heavy-duty vehicle platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for follower vehicles compared with the use of standard platoon controllers.

Model Reduction for Nonlinear Systems by Incremental Balanced Truncation

In this paper, the method of incremental balanced truncation is introduced as a tool for model reduction of nonlinear systems. Incremental balanced truncation provides an extension of balanced truncation for linear systems towards the nonlinear case and differs from existing nonlinear balancing techniques in the definition of two novel energy functions. These incremental observability and incremental controllability functions form the basis for a model reduction procedure in which the preservation of stability properties is guaranteed. In particular, the property of incremental stability, which provides a notion of stability for systems with nonzero inputs, is preserved. Moreover, a computable error bound is given. Next, an extension towards so-called generalized incremental balanced truncation is proposed, which provides a reduction technique with increased computational feasibility at the cost of a (potentially) larger error bound. The proposed reduction technique is illustrated by means of application to an example of an electronic circuit with nonlinear elements.

Cyber–Physical Control of Road Freight Transport

Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total 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.

Model Reduction for a Class of Convergent Nonlinear Systems

In this technical note, a model reduction procedure is presented for nonlinear systems that can be decomposed into a feedback interconnection of a linear and nonlinear subsystem. Conditions for stability of the reduced-order model and an error bound are given. Herein, the input-to-state convergence property is exploited, which proves to be useful in the definition and derivation of the error bound. The results are illustrated by application to a nonlinear mechanical system.

Model reduction of networked passive systems through clustering

In this paper, a model reduction procedure for a network of interconnected identical passive subsystems is presented. Here, rather than performing model reduction on the subsystems, adjacent subsystems are clustered, leading to a reduced-order networked system that allows for a convenient physical interpretation. The identification of the subsystems to be clustered is performed through controllability and observability analysis of an associated edge system and it is shown that the property of synchronization (i.e., the convergence of trajectories of the subsystems to each other) is preserved during reduction. The results are illustrated by means of an example.

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

The operation of groups of heavy-duty vehicles at close intervehicular distances (known as platoons) has been shown to be an effective way of reducing fuel consumption. For single vehicles, it is also known that the availability of preview information on the road topography can be exploited to obtain fuel savings. The current paper aims at the inclusion of preview information in platooning by introducing a two-layer control system architecture for so-called look-ahead platooning. Here, the layers are responsible for the inclusion of preview information and real-time vehicle control for platooning, respectively. Within this framework, a control strategy is presented, where dynamic programming is used for the calculation of fuel-optimal speed profiles, while a model predictive control approach is exploited for the real-time vehicle control. The feasibility of this approach is illustrated by means of the simulation of relevant scenarios.

Analysis and Control of Stick-Slip Oscillations in Drilling Systems

This paper proposes feedback control strategies for the mitigation of torsional stick-slip oscillations in drilling systems using drag bits. Herein, we employ a model for the coupled axial-torsional drill-string dynamics in combination with a rate-independent bit-rock interaction law including both cutting and frictional effects. Using a singular perturbation and averaging approach, we show that the dynamics of this model generate an apparent velocity-weakening effect in the torque-on-bit, explaining the onset of torsional stick-slip vibrations. Based on this dynamic analysis, the (decoupled) torsional dynamics can be described by a delay-differential equation with a state-dependent delay. Using this model, we propose both state- and output-feedback control strategies for the mitigation of torsional stick-slip oscillations, where the latter strategy uses surface measurements only. The effectiveness of the proposed approaches is shown in a simulation study.

Clustering-Based Model Reduction of Networked Passive Systems

The model reduction problem for networks of interconnected dynamical systems is studied in this paper. In particular, networks of identical passive subsystems, which are coupled according to a tree topology, are considered. For such networked systems, reduction is performed by clustering subsystems that show similar behavior and subsequently aggregating their states, leading to a reduced-order networked system that allows for an insightful physical interpretation. The clusters are chosen on the basis of the analysis of controllability and observability properties of associated edge systems, representing the importance of the couplings and providing a measure of the similarity of the behavior of neighboring subsystems. This reduction procedure is shown to preserve synchronization properties (i.e., the convergence of the subsystem trajectories to each other) and allows for the a priori computation of a bound on the reduction error with respect to external inputs and outputs. The method is illustrated by means of an example of a thermal model of a building.

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

The formation of groups of closely-spaced heavy-duty vehicles, known as platoons, reduces the overall aerodynamic drag and therefore leads to reduced fuel consumption and reduced greenhouse gas emissions. This paper focuses on the optimal control of merging maneuvers for the formation of a growing platoon. Hereto, the merging problem is formulated as a hybrid optimal control problem and an algorithm for the computation of optimal merging times and corresponding optimal vehicle trajectories is developed by exploiting an extension of Pontryagins maximum principle. Moreover, a model predictive control approach on the basis of this algorithm is presented that makes the merging maneuvers robust to modelling uncertainties and external disturbances. The results are illustrated by evaluating a scenario involving three vehicles, which indicates fuel savings of up to 13% with respect to the vehicles driving alone.