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

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

Design and Simulation of Integrated Lateral and Longitudinal Controller for Automated and Connected Vehicles

This thesis is concerned with the design of an integrated longitudinal and lateral controller for a platoon of connected and automated vehicles executing an emergency lane change maneuver. The efficacy of the designed controller is shown using a simulation setup that has been built using an open-source simulator, called TORCS, in conjunction with Simulink, where the controller is designed. The longitudinal controller component consists of an Adaptive Cruise Control (ACC) System that employs a Constant Time Headway (CTH) policy. The lateral controller is based on the lead vehicle in a platoon broadcasting its GPS coordinates to all the following vehicles at regular intervals of time. This information is utilized by every following vehicle to construct a desired trajectory to track (in particular, it is a circular spline). Since the same information is used by every following vehicle, the issue of propagation of errors in the lateral direction is circumvented. Stability robustness to variation in mass and inertia of the vehicle is crucial for ensuring that the controller can be implemented in practice. Vehicle mass and moment of inertia are most affected by the addition on passengers and cargo. The complicating part of studying the robustness stems from the characteristic polynomial being a rational function of the vehicle mass and is not readily amenable to analysis by the well-known root-locus method. A D-decomposition based method was adopt to find the maximum speed for safely/stably tracking a given reference trajectory

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Cooperative Collision Avoidance in a Connected Vehicle Environment

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles. It leverages the Vehicle to Everything (V2X) communication technology to create a real-time implementable collision avoidance algorithm along with decision-making for a vehicle that communicates with other vehicles. Four distinct collision risk environments are simulated on a cost effective Connected Autonomous Vehicle (CAV) Hardware in the Loop (HIL) simulator to test the overall algorithm in real-time with real electronic control and communication hardware