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 in Engineering, University of Akron, 2015, Electrical Engineering

The modeling, simulation and implementation of a range extender for an existing truck are presented in this document. The objective of this thesis is to re-engineer an existing electric truck into a series hybrid electric vehicle through a range extender. A LiFePO4 (Li-Ion) battery pack powered electric vehicle is used as a platform to implement a range extender using an advanced control strategy. A range extended electric vehicle has been simulated using series hybrid electric vehicle architecture to size the range extender by studying the behavior of the system under different drive cycles. To determine the size of the range extender, a specific drive cycle in which the vehicle is considered to be cruising at 65 Mph was selected to study the operation of the range extended electric vehicle. By analyzing the results of the simulations it has been concluded that a 30 kW engine and generator set is an appropriate size of the range extender to design a range extended electric vehicle. The range extender was designed, simulated and tested at a bench before it was implemented on a vehicle. A 30 kW range extender was developed by mechanically coupling a 40 hp V-twin horizontal shaft gasoline engine with a 30 kW permanent magnet generator from one of the electrical machines in the transmission of 2004 Toyota Prius. A range extended electric vehicle control algorithm was developed to control the operation of the engine and generator set relative to the state of charge (SOC) of the battery pack. The main objective of the developed algorithm is to maintain the SOC of the battery pack between a certain limits predefined by the programmer. It was determined that by maintaining the iii SOC of the battery pack in between 60% to 80% the targeted distance of 100 miles was achieved with 2 gallons of the gasoline. A novel power converter was developed to convert three phase AC output of the generator into an appropriate DC voltage to charge the battery pack. (open full item for complete abstract)

Committee: Yilmaz Sozer Dr (Advisor); Malik Elbuluk Dr. (Committee Member); Tom Hartley Dr. (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Engineering