Master of Science, The Ohio State University, 2020, Mechanical Engineering

The air we breathe is getting dangerously polluted with passenger vehicles and heavy-duty vehicles being one of the major sources of this pollution, producing significant amounts of nitrogen oxides, carbon monoxide, and other harmful gases. The U.S. Environmental Protection Agency (EPA) has laid stringent rules and aggressive policies to curb this pollution. Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV) are a promising option considering their efficient operation and reduced emissions. These technologies are being developed at a rapid pace and can occupy a significant place in the automotive market. Companies are investing heavily to enhance the skills of future generation of engineers to develop these technologies through student competitions and workshops. EcoCAR Mobility Challenge (ECMC), a four-year Advanced Vehicle Technology Competition (AVTC) is one-way companies are pursuing this challenge. ECMC challenges teams to apply advanced propulsion systems, as well as connected and automated vehicle technology to improve the energy efficiency, safety, and consumer appeal of a 2019 Chevrolet Blazer – specifically for the carsharing market. The work described in this thesis focuses on the Model Based design approach adopted for the vehicle plant model and controls development during years one and two of the competition. The process includes the vehicle architecture selection process, component and soft ECU model development and finally describes the framework developed for testing of the control algorithm using an example of a fault scenario.

Committee: Shawn Midlam-Mohler Dr. (Advisor); Giorgio Rizzoni Dr. (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

With the on-going electrification and data-intelligence trends in logistics industries, enabled by the advances in powertrain electrification, and connected and autonomous vehicle technologies, the traditional ways vehicles are designed by engineering experience and sales data are to be updated with a design for operation notion that relies intensively on operational data collection and large scale simulations. In this work, this design for operation notion is revisited with a specific combination of optimization and control techniques that promises accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pick-up and delivery truck that is limited to a given driving mission. A Gaussian Process (GP) based statistical learning approach is used to refine the search for the most accurate, optimal designs. Five hybrid powertrain architectures are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. A cross-architecture performance and cost comparison is performed and the selected architecture is developed further in the form of a forward simulator with a dedicated ECMS controller. In the end, a traffic-in-the-loop simulation is performed by integrating the selected powertrain architecture with a SUMO traffic simulator to evaluate the performance of the developed controller against varying driving conditions.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering; Sustainability; Systems Design; Transportation

Master of Science (MS), Wright State University, 2017, Computer Science

A cognitive system attempts to achieve its goals by utilizing the appropriate resources present to yield the best possible outcome within a short duration. To achieve the goals in such an efficient manner, it is important for the agent to manage its goals well. Goal management not only makes the agent efficient but also flexible, more durable to the sudden changes in the environment, and self-reliant. Goal Management consists of various goal operations including goal formulation, selection, change, delegation, achievement, and monitoring. Each operation is unique and has its own significance in aiding the performance of the agent. The thesis work focuses on the implementation of two particular goal operations. These are goal selection and goal change with concentration of the former. Goal selection allows the agents to choose among its goals by using any criteria which are appropriate for the domain. Goal change allows the agent to change its current goal to another goal because of reasons like the inadequate amount of resources or detection of a discrepancy. The implementation of these operations is done within a cognitive architecture called the Metacognitive Integrated Dual-Cycle Architecture in the two problem domains of construction and restaurant. In the construction domain, the goals are to construct the towers using the resources within a provided time limit, and in the restaurant domain, the goals are to satisfy the maximum number of people by serving items ordered with a limited amount of money. After the implementation of goal selection and goal change, the work is evaluated using various methods, one of which is the comparison of the performance of MIDCA with and without those goal change operations and the other is by comparing two different goal selection methods. Several graphical depictions and mathematical formulae are presented that support the course of performance comparison.

Committee: Michelle Cheatham Ph.D. (Committee Chair); Michael Cox Ph.D. (Committee Co-Chair); Mateen Rizki Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science