336 research outputs found
Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning
The operation of groups of heavy-duty vehicles (HDVs) at a small
inter-vehicular distance (known as platoon) allows 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 HDVs,
slopes have a significant impact on the feasible and optimal speed profiles
that each vehicle can and should follow. Therefore 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 HDV 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 the follower vehicles compared to the use of standard platoon controllers.Comment: 16 pages, 16 figures, submitted to journa
Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming
Platooning connected and autonomous vehicles (CAVs) can improve traffic and
fuel efficiency. However, scalable platooning operations require junction-level
coordination, which has not been well studied. In this paper, we study the
coordination of vehicle platooning at highway junctions. We consider a setting
where CAVs randomly arrive at a highway junction according to a general renewal
process. When a CAV approaches the junction, a system operator determines
whether the CAV will merge into the platoon ahead according to the positions
and speeds of the CAV and the platoon. We formulate a Markov decision process
to minimize the discounted cumulative travel cost, i.e. fuel consumption plus
travel delay, over an infinite time horizon. We show that the optimal policy is
threshold-based: the CAV will merge with the platoon if and only if the
difference between the CAV's and the platoon's predicted times of arrival at
the junction is less than a constant threshold. We also propose two
ready-to-implement algorithms to derive the optimal policy. Comparison with the
classical value iteration algorithm implies that our approach explicitly
incorporating the characteristics of the optimal policy is significantly more
efficient in terms of computation. Importantly, we show that the optimal policy
under Poisson arrivals can be obtained by solving a system of integral
equations. We also validate our results in simulation with Real-time Strategy
(RTS) using real traffic data. The simulation results indicate that the
proposed method yields better performance compared with the conventional
method
Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization
A method for reducing the fuel consumption of a platoon of heavy-duty vehicles (HDVs) is described and evaluated in simulations for homogeneous and heterogeneous platoons. The method, which is based on speed profile optimization and is referred to as P-SPO, was applied to a set of road profiles of 10 km length, resulting in fuel reduction of 15.8% for a homogeneous platoon and between 16.8% and 17.4% for heterogeneous platoons of different mass configurations, relative to the combination of standard cruise control (for the lead vehicle) and adaptive cruise control (for the follower vehicle). In a direct comparison with MPC-based approaches, it was found that P-SPO outperforms the fuel savings of such methods by around 3 percentage points for the entire platoon, in similar settings. In P-SPO, unlike most common platooning approaches, each vehicle within the platoon receives its own optimized speed profile, thus eliminating the intervehicle distance control problem. Moreover, the P-SPO approach requires only a simple vehicle controller, rather than the two-layer control architecture used in MPC-based approaches
Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing
This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow.
The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios
Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks
Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters
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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Fuel-efficient driving strategies
This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches
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