Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed

This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie

Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.Comment: 40 pages, 9 figure

Integrated Charging Scheduling and Operational Control for an Electric Bus Network

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. We propose a hierarchical control framework to solve this problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions together with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.Comment: 29 pages, 9 figure

Structure and Dynamics in Liquid Battery Electrolytes

The introduction of Sony’s rechargeable lithium-ion battery in 1991 sparked a transformation of our everyday life, enabling wide-spread use of portable electronics, such as smartphones and laptops. Furthermore, in recent years the increased usage of electrical vehicles and the on-going change to transient renewable energy sources has created a large interest in cheaper, safer, more sustainable, long-lasting and energy denser batteries. Next generation batteries – batteries beyond the traditional lithium-ion battery chemistries – offers possible routes towards the for-mentioned sought performance, societal and economical improvements. In this thesis several next generation battery concepts are studied. In particular, i) the sodium-ion battery, offering similar energy densities to that of the modern-day lithium-ion battery, but showing better power performance, is cheaper, more sustainable and safer, and ii) highly concentrated electrolytes, enabling higher energy densities, improved safety features, and improved cycling stability, are studied.\ua0Several of the improvements in safety and performance seen in these next generation battery technologies stem from the local environment in the electrolyte. In this work I present a comprehensive study of the local cationic environment in several next generation battery electrolytes employing computational methods such as semi-empirical methods, density functional theory, and ab initio molecular dynamics. Furthermore, novel methods for studying the dynamics of the solvation shell are presented. The results of these studies are compared to what I and others have found in conventional lithium-ion battery electrolytes, and the connection between the local electrolyte structure and dynamics and the macroscopic electrolyte and battery properties is discussed

Phase field modeling of electrodeposition process in lithium metal batteries

One of the main weaknesses in long term performance of conventional lithium batteries is the growth of lithium microstructures on the electrode surface due to an electrochemical process, which can eventually lead to failure of these batteries. Suppressing this microstructure growth is a key in developing new generations of lithium metal batteries (LMBs). In this study, a two-dimensional (2D) phase field model is constructed to understand and determine the parameters controlling formation and evolution of microstructures in LMBs. A Ginzburg-Landau free energy functional, which is a function of concentration of Li+ and applied voltage, and a system consisting of a pure lithium metal electrode and an electrolyte made of lithium hexafluorophosphate in a binary organic solvent of 1:1 ratio of ethylene carbonate and dimethyl carbonate. The evolution equations consist of a Cahn-Hilliard fourth-order partial differential equation (PDE) for evolution of Li+ concentration in the domain, and a Laplace\u27s equation for charge conservation. Using COMSOL, the growth thickness and growth rate from the anode surface are simulated by applying different boundary conditions of concentration and different potentials. The proposed model is compared to existing electrodeposition models and results show that the Laplace\u27s equation can be used in different forms proposed in literature. Assuming this battery to be a strongly anisotropic system, the Cahn-Hilliard equation is modified to include anisotropy and the simulations reveal a strong directional growth from the anode surface. The results of the developed model suggested that this phase field model is capable of quantitatively predicting the formation and growth of microstructures in LMBs and may be used to improve their life time in the future. This model can also be applied to study electrodeposition process in other material systems --Abstract, page iii

Ermittlung des Energiebedarfs zur Bewegung von Fahrzeugen in mikroskopischen Verkehrssimulationen

Die Integration von Modellen für Fahrzeuge mit alternativen Antrieben in Verkehrssimulationen erfordert eine genauere Betrachtung der Energieflüsse in den einzelnen Fahrzeugen. Diese Arbeit betrachtet den Energiebedarf für die Bewegung von Fahrzeugen und evaluiert vorhandene klassische Modelle zur Abstraktion der physikalischen Einflüsse. Aufgrund der fehlenden Einstimmigkeit der Autoren bei der Beschreibung solcher Modelle in der Literatur wird letztlich der Ansatz verfolgt, ein entsprechendes Modell von der physikalischen Basis ausgehend neu zu entwickeln. Zusätzlich dazu wird festgestellt, dass die Beschränkungen der geläufigen Verkehrssimulationsumgebungen einen signifikanten Einfluss auf die Berechenbarkeit einzelner Komponenten derartiger Modelle haben. Das geschaffene Modell wird anschließend in verschiedenen Varianten in einem Vergleich mit einem weit verbreiteten Modell evaluiert. Zu guter Letzt muss konstatiert werden, dass eine Erhöhung der Realitätsnähe der Simulation – insbesondere im situativen Bereich – erreicht werden konnte, für wesentliche Verbesserungen jedoch eine Beseitigung bestehender Restriktionen der Simulationsumgebungen erforderlich wäre.The integration of models for alternative fuel vehicles in traffic simulation requires a closer examination of the energy flows in the individual vehicles. This work considers the energy required for the movement of vehicles and evaluates existing classical models for the abstraction of the physical influences. Due to the lack of unanimity of the authors in the discribing of such models in the literature, the approach ultimately pursued requires redeveloping an appropriate model of the physical basis as a starting point. Additionally, it has been established that the restrictions of the common traffic simulation environments have a significant impact on the computability of individual components of such models. The created model is then evaluated in different variants in comparison with a widely used model. Finally, it is notable that increasing simulation realism could be achieved, however, for in order to see substantial improvements, the elimination of certain restrictions of current simulation environments would be required

Multi-Scale Modeling, Surrogate-Based Analysis, and Optimization of Lithium-Ion Batteries for Vehicle Applications.

A common attribute of electric-powered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid- and fully-electric aircraft, and satellites is that their performance is usually limited by the energy density of their batteries. Although lithium-ion batteries offer distinct advantages such as high voltage and low weight over other battery technologies, they are a relatively new development, and thus significant gaps in the understanding of the physical phenomena that govern battery performance remain. As a result of this limited understanding, batteries must often undergo a cumbersome design process involving many manual iterations based on rules of thumb and ad-hoc design principles. A systematic study of the relationship between operational, geometric, morphological, and material-dependent properties and performance metrics such as energy and power density is non-trivial due to the multiphysics, multiphase, and multiscale nature of the battery system. To address these challenges, two numerical frameworks are established in this dissertation: a process for analyzing and optimizing several key design variables using surrogate modeling tools and gradient-based optimizers, and a multi-scale model that incorporates more detailed microstructural information into the computationally efficient but limited macro-homogeneous model. In the surrogate modeling process, multi-dimensional maps for the cell energy density with respect to design variables such as the particle size, ion diffusivity, and electron conductivity of the porous cathode material are created. A combined surrogate- and gradient-based approach is employed to identify optimal values for cathode thickness and porosity under various operating conditions, and quantify the uncertainty in the surrogate model. The performance of multiple cathode materials is also compared by defining dimensionless transport parameters. The multi-scale model makes use of detailed 3-D FEM simulations conducted at the particle-level. A monodisperse system of ellipsoidal particles is used to simulate the effective transport coefficients and interfacial reaction current density within the porous microstructure. Microscopic simulation results are shown to match well with experimental measurements, while differing significantly from homogenization approximations used in the macroscopic model. Global sensitivity analysis and surrogate modeling tools are applied to couple the two length scales and complete the multi-scale model.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99980/1/wenbodu_1.pd

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization

From Multiple Scale Modeling to Multiscale-Modeling

To power a sustainable future, interest in battery research and technology is at an all time high. In order to enable a transition to green tech many applications, such as the automotive industry, is in need of higher power densities, energy densities, longer life-times, and safer batteries.\\One crucial component of batteries is the electrolyte, which for lithium-ion batteries (LIBs) have not developed as much as one would expect since its introduction in the 1990s. Through the use of novel electrolyte concepts such as highly concentrated electrolytes (HCE) and localized highly concentrated electrolytes (LHCE) desired qualities such as an increased energy density could be achieved. The effects of local properties on macroscopic behaviour within these systems are much more striking than conventional LIB electrolytes, constraining the use of common simulation techniques used in battery research.This thesis studies these novel electrolyte concepts using an array of different computational methods, such as DFT, AIMD, and classical MD. Based on these techniques, as well as on the CHAMPION method, the work done in this thesis attempts to develop a method for tying together understanding of materials physics at the different scales represented by AIMD and classical MD through force sampling. This force sampling is presented as an alternative to commonplace MD force fields such as AMBER, CHARMM and GROMACS. Finding the local structure important for explaining global transport phenomenon by showing that local HCE structure is retained when going from HCE to LHCE as well as showing the possibility for these new types of FFs, even though more work is needed on the accuracy of these FFs

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information