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

Fuel Consumption Reduction Through Velocity Optimization for Light-Duty Autonomous Vehicles with Different Energy Sources

The emergence of self-driving cars provides an additional flexibility to the vehicle controller, by eliminating the driver and allowing for control of the vehicle's velocity. This work employs constrained optimal control techniques with preview of position constraints, to derive optimal velocity trajectories in a longitudinal vehicle following mode. A framework is developed to compare autonomous driving to human driving, i.e. the Federal Test Procedures of the US Environmental Protection Agency. With just velocity smoothing, improvements by offline global optimization of up to 18% in Fuel Economy (FE), are shown for certain drive cycles in a baseline gasoline vehicle. Applying the same problem structure in an online optimal controller with 1.5 s preview showed a 12% improvement in FE. This work is further extended by using a lead velocity prediction algorithm that provides inaccurate future constraints. For a 10 s prediction horizon, a 10% improvement in FE has been shown. A more conventional procedure for achieving velocity optimization would be the minimization of energy demand at the wheels. This method involves a non-linear model thus increasing optimization complexity and also requires additional information about the vehicle such as mass and drag coefficients. It is shown that even though tractive energy minimization has a lower energy demand than velocity smoothing, smoothing works as well if not better when it comes to reducing fuel consumption. These results are shown to be valid in simulation across three different engines ranging from 1.2 L-turbocharged to 4.3 L-naturally aspirated. The implication of these results is that tractive energy minimization requiring more complex control does not work well for conventional gasoline vehicles. It is further shown that using reduced order powertrain models currently found in literature for velocity optimization, can result in worse FE than previous optimizations. Therefore, an easily implementable, vehicle agnostic velocity smoothing algorithm could be preferred for drive cycle optimization. Employing these same velocity optimization techniques for a battery electric vehicle (BEV) can increase battery range by 15%. It is further demonstrated that eco-driving and regenerative braking are not complimentary and eco-driving is always preferred. Finally, power split optimization has been carried out for a fuel cell hybrid, and it has been shown that a rule-based strategy with drive cycle preview could match the global optimal results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149826/1/niketpr_1.pd