Date of Award
Doctor of Philosophy (PhD)
Thanks to advanced technologies like Connected and Autonomous Vehicles, platooning is becoming more and more useful as a method to potentially increase road capacity and reduce energy consumption. While there are many studies in the literature reporting significant fuel and energy savings as a result of platooning, these studies are ignoring the extra energy required to maintain vehicles in close formation referred to as string stability. Also, there are other factors many of the current studies are not considering such as the position of a vehicle in a platoon, the background traffic that may complicate the process of forming platoons, and the vehicle type. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this study, we develop a simulation-optimization framework to tackle this challenge. The simulation model simulates real traffic conditions for individual vehicles and platoons. Additionally, the simulation model implements platoon forming decisions obtained from an optimization model. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. Our optimization model determines vehicle-to-platoon assignments given the locations, speed, and acceleration of vehicles and platoons. Particularly, we concentrate two different optimization models. One is a centralized model to make platooning decisions with aim to maximize potential energy savings system-wide. On the other hand, a decentralized model utilizing a competition game is developed to make decisions for individual vehicle energy saving purpose. In addition to the simulation- optimization framework, an accurate energy consumption model is developed, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. In addition to the centralized and decentralized decision making models, we track a single platoon to observe the energy consumption for this one platoon under different parameters in order to better understand the factors that impact energy savings. Our results show that a system-wide savings of about 3% in centralized model, and 1.5% in decentralized model can be realized over 100 miles when platoons are formed strategically. Comparison between two models also confirm, as expected, that the centralized model forms better platoons in terms of energy savings.
Liu, Dahui, "Optimizing Energy Savings for a Fleet of Commercial Autonomous Vehicles via Centralized and Decentralized Platooning Decisions" (2020). All Dissertations. 2689.