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

PhD, University of Cincinnati, 2008, Arts and Sciences : Psychology

Vigilance or sustained attention is a critical aspect of many jobs including air-traffic control, medical screening/monitoring, and detection of illicit radioactive material at seaports and border crossings. An extensive review by Reinerman (2006) concluded that traditional approaches to personnel selection encompassing sensory acuity, aptitude, sex, age, and personality measures for tasks requiring sustained attention have been ineffective. The present study utilized the methodology from Reinerman et al. (2006), which attacked the selection issue using responses to a brief 10-min screening battery involving high workload tracking, verbal working-memory, and line discrimination tasks to predict performance on a subsequent sensory vigilance task. Two predictors of interest were cerebral blood flow velocity, measured via transcranial Doppler ultrasonography (Warm and Parasuraman, 2007) and subjective state, as indexed by the Dundee Stress State Questionnaire (Matthews et al; 2002). The present vigilance task was a letter transformation working-memory task and was composed of four consecutive 9-min periods. Such cognitive vigilance tasks require different information-processing components and responses than those of sensory tasks. The aim for the present study was to generalize the findings of Reinerman et al. (2006) to that of cognitive vigilance. Multiple regression (R = .577) indicated that higher levels of CBFV in the left and right hemispheres and higher post-battery task engagement scores on the DSSQ during performance of the screening battery predicted perceptual sensitivity (A') during the final period of watch when performance deficiencies are most likely to occur. Predictions from a correlation of this magnitude, which accounts for 28.1 percent of the variance when adjusted for shrinkage, can lead to an increase in job success rate of 40 to 60 percent (Rosenthal and Rubin, 1982). These findings were interpreted theoretically in light of a resource-workload mo (open full item for complete abstract)

Committee: Joel Warm PhD (Committee Chair); Matthews Gerald PhD (Committee Co-Chair); Stutz Robert PhD (Committee Member) Subjects: Psychology