Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Multicriteria cruise control design considering geographic and traffic conditions

The paper presents the design of cruise control systems considering road and traffic information during the design of speed trajectories. Several factors are considered such as road inclinations, traffic lights, preceding vehicles, speed limits, engine emissions and travel times. The purpose of speed design is to reduce longitudinal energy, fuel consumption and engine emissions without a significant increase in travel time. The signals obtained from the road and traffic are handled jointly with the dynamic equations of the vehicle and built into the control design of reference speed. A robust H∞ control is designed to achieve the speed of the cruise control, guaranteeing the robustness of the system against disturbances and uncertainties

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

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

Saving Fuel for Heavy-Duty Vehicles Using Connectivity and Automation

The booming of e-commerce is placing an increasing burden on freight transport system by demanding faster and larger amount of delivery. Despite the variety in freight transport means, the dominant freight transport method is still ground transport, or specifically, transport by heavy-duty vehicles. Roughly one-third of the annual ground freight transport expense goes to fuel expenses. If fuel costs could be reduced, the finance of freight transport would be improved and may increase the transport volume without additional charge to average consumers. A further benefit of reducing fuel consumption would be the related environmental impact. The fuel consumption of the heavy-duty vehicles, despite being the minority of road vehicles, has a major influence on the whole transportation sector, which is a major contributor to greenhouse gas emissions. Thus, saving fuel for heavy-duty trucks would also reduce greenhouse gas emission, leading to environmental benefits. For decades, researchers and engineers have been seeking to improve the fuel economy of heavy-duty vehicles by focusing on vehicles themselves, working on advancing the vehicle design in many aspects. More recently, attention has turned to improve fuel efficiency while driving in the dynamic traffic environment. Fuel savings effort may be realized due to advancements in connected and automated vehicle technologies, which provide more information for vehicle design and control. This dissertation presents state-of-the-art techniques that utilize connectivity and automation to improve the fuel economy of heavy-duty vehicles, while allowing them to stay safe in real-world traffic environments. These techniques focus on three different levels of vehicle control, and can result in significant fuel improvements at each level. Starting at the powertrain level, a gear shift schedule design approach is proposed based on hybrid system theory. The resulting design improves fuel economy without comprising driveability. This new approach also unifies the gear shift logic design of human-driven and automated vehicles, and shows a large potential in fuel saving when enhanced with higher level connectivity and automation. With this potential in mind, at the vehicle level, a fuel-efficient predictive cruise control algorithm is presented. This mechanism takes into account road elevation, wind, and aggregated traffic information acquired via connectivity. Moreover, a systematic tool to tune the optimization parameters to prioritize different objectives is developed. While the algorithm and the tool are shown to be beneficial for heavy-duty vehicles when they are in mild traffic, such benefits may not be attainable when the traffic is dense. Thus, at the traffic level, when a heavy-duty vehicle needs to interact with surrounding vehicles in dense traffic, a connected cruise control algorithm is proposed. This algorithm utilizes beyond-line-of-sight information, acquired through vehicle-to-vehicle communication, to gain a better understanding of the surrounding traffic so that the vehicle can response to traffic in a fuel efficient way. These techniques can bring substantial fuel economy improvements when applied individually. In practice, it is important to integrate these three techniques at different levels in a safe manner, so as to acquire the overall benefits. To achieve this, a safety verification method is developed for the connected cruise control, to coordinate the algorithms at the vehicle level and the traffic level, maximizing the fuel benefits while staying safe.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147523/1/hchaozhe_1.pd