Master of Science, The Ohio State University, 2018, Mechanical Engineering

This thesis details efforts towards developing a methodology that enables the design of a look ahead based energy management system. It explores various technologies that are required to enable such a system to function on a physical vehicle. A new simulation framework known as `Traffic-In-Loop' (TIL) simulation is developed to mimic real-world driving. It serves as a drive cycle independent controls development platform. The framework is enabled by combining microscopic traffic simulation with a detailed mathematical powertrain model. The TIL simulation technique facilitates emulation of on-board sensors, V2X communication and capture causal behavior of real-world scenarios. Data collected from these virtual sensors are used to forecast future drive scenarios -- called `Look ahead predictions'. Further a strategy to integrate future drive scenario forecasts with powertrain control is introduced. The above advances, catalyzed the design of a look ahead based energy management controller, called 'Delta Energy Controller'. It aims at improving a vehicle's fuel economy by utilizing available drive scenario forecasts. Simulation results are used to prove the optimality of this controller and study the improvement in fuel economy as a function of better look ahead predictions.

Committee: Giorgio Rizzoni (Advisor); Marcello Canova (Committee Member); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering; Transportation

Doctor of Philosophy, The Ohio State University, 2018, Mechanical Engineering

Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. They use both electrical and traditional fossil-fuel based energy sources, which makes them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, energy management systems are designed, prototyped, and validated in a software simulation environment before implementation on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and cost. Traditionally, the design of energy management systems is based on statutory drive-cycles. Drive-cycle based solutions to energy management systems improve fuel economy of the vehicle and are well suited for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identifies and solves three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed. The simulation framework includes traffic simulation and powertrain simulation capabilities. It is termed traffic integrated powertrain co-simulation. Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. Finally, through the use of optimal c (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Shawn Midlam-Mohler PhD (Committee Member); David Hoelzle PhD (Committee Member); Abhishek Gupta PhD (Committee Member); Qadeer Ahmed PhD (Committee Member) Subjects: Mechanical Engineering; Transportation