Master of Science, The Ohio State University, 2022, Mechanical Engineering

An efficient simulation framework for co-optimization of design and control is fundamental in the development phase of hybrid electric vehicles to achieve the best system- level improvements of energy efficiency and emissions. Coordination schemes for co- optimization have been widely investigated in the literature, but only for a limited number and nature of design and control variables. In this study a decomposition-based coordination scheme capable to handle multi-time scale, time variant and time invariant (discrete and continuous) variables with ability to handle each sub-problem with different solver is not only demonstrated but also compared with simultaneous-based scheme in terms of optimality of the solution and computational performance. The two coordination schemes are used to co-optimize energy management strategy and components sizing for a series hybrid truck. In addition, multiple objectives are weighted in the cost function: fuel consumption, battery size, and tailpipe pollutant emissions. Results show that the simultaneous scheme is computationally less expensive for simple problems, but it becomes computationally inefficient with increasing problem complexity, with the additional drawback of not being able to handle integer-valued dynamic variables. On the other hand, the decomposition-based scheme can solve such problems, but with a more complex problem formulation. Results show that the decomposition-based scheme has not only 14% improvement in computational performance, but the optimality of the solution is also comparable with simultaneous-based scheme. Hence, as compared to the dynamic optimization, co-optimization yields up to 3.7% improvement in the average genset efficiency operation. Moreover, the fuel consumption for dynamic optimization was 2.5 kg which is reduced to 1.6 kg with co-optimization and was further reduced to 1.5 by adding engine on off control.

Committee: Qadeer Ahmed (Advisor); Shawn Midlam-Mohler (Committee Member); Manfredi Villani (Other) Subjects: Aerospace Engineering; Automotive Engineering; Electrical Engineering; Mechanical Engineering; Robotics

Master of Science, The Ohio State University, 0, Electrical and Computer Engineering

The transportation sector is making a transition from conventional engine vehicles to hybrid electric vehicles because of the environmental concerns like global warming. HEVs are a very lucrative option today because it helps reduce the usage of fossil fuels without much compromise on the range of the vehicle. This is because HEVs offer extra degrees of freedom to operate the vehicle in electric mode or engine mode or both. This calls for optimizing the powertrain of a HEV. As a part of this research work, we present a more realistic approach by considering a large state-space which engenders complex dynamics/ interactions between multiple sub-systems. A P2 parallel hybrid powertrain of a class 6 Pick-up & Delivery truck is considered as the case-study problem. This problem involves 13 states and 4 control levers. Some of these variables are discrete in nature and some are continuously varying with respect to time. Some have slow dynamics like temperature, while some have fast dynamics like battery state of charge which makes it a stiff system. Usage of LUTs, interpolations and conditional formulations exacerbate the complexity of the problem already considered. Optimization of all these variables together makes it very challenging for the solver hence, a novel three-step approach is presented and used to solve the case-study problem. This makes use of pseudo spectral method (PSC) for handling real-valued variables and for accurate state estimations and Dynamic programming (DP) for the optimization of integer-valued variables. We present three scenarios for the case-study problem where fuel consumption alone is minimized, emissions alone are minimized and, lastly a combination of both fuel and emissions are minimized. The computation time for this huge problem is only of the order of 50-80 minutes using the 3-step approach. The fuel minimization case has the least fuel and highest emissions, and vice versa for the emissions minimization case. The fuel & emissions pr (open full item for complete abstract)

Committee: Qadeer Ahmed (Advisor); Vadim Utkin (Committee Member) Subjects: Aerospace Engineering; Automotive Engineering; Electrical Engineering; Mechanical Engineering; Robotics

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

Within this thesis document we focus on the application of Nonlinear Model Predictive Control (NMPC) onto an epidemic compartmental model. The compartmental model is a partial differential equation (PDE) based Susceptible Latent Infected Recovered (SLIR) epidemic model. This model serves as the basis of the NMPC. In order to generate the necessary parameters for initializing and training the use of constrained optimization, a single-objective Genetic Algorithm (GA), and LSTM (Long-Short-Term-Memory) deep learning were explored. The spatial domains considered for the SLIR epidemic model includes Hamilton County, Ohio as well as the entire state of Ohio, USA. With respect to Hamilton County, Ohio three different time periods were evaluated in which varied levels of infection relating to COVID-19 were observed. At the state wide level only one time period was consider. The NMPC considers two control schemes. The first being control applied uniformly across the spatial domain of interest. While the second focuses on applying the control in a spatially targeted manner to specific geographical areas based on observed higher levels of infection. The NMPC also employs a cost function comprising the infection spread density and the associated cost of applied control measures. The latter of which in turn representing socioeconomic effects. Overall, the NMPC framework developed here is intended to aid in the evaluation of optimal Non-Pharmaceutical Interventions (NPI) towards spread mitigation of infectious diseases.

Committee: Manish Kumar Ph.D. (Committee Chair); Shelley Ehrlich M.D. (Committee Member); Subramanian Ramakrishnan Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The COVID-19 pandemic highlighted the need for improved and precise prediction of the spatiotemporal trends of epidemic transmission. An optimized epidemic model is crucial for effectively forecasting flow of infection. By optimizing the model parameters, they can provide valuable insights into the dynamics of infection transmission and this degree of tuning helps health officials and policymakers to make data-driven decisions regarding disease control strategies, allocation of resources, and planning for healthcare. Therefore, it highlights the need of implementing reliable optimizing strategies in case of epidemic models. Similarly, the basic and effective reproductive numbers (R0, Re) are quantitative metrics widely used for estimating the rate at which the infection propagates. The limitations of existing techniques for estimating R0 and Re points the need for novel approaches to accurately estimate them using the available data. This initial part of this study presents the development of a custom GA which is capable of efficiently searching for the parameters of an epidemic model in any specified geographical region and time period. Following this, a novel computational framework for predicting the reproduction numbers from true infection data has been presented. The computational framework is derived from a reaction-diffusion based PDE epidemic model which involves fundamental mathematical derivations for obtaining their values. The PDE model is optimized using the proposed GA and the model output using the optimized parameters is found to be in correspondence with the ground truth COVID-19 data of Hamilton county, Ohio. Subsequently, the established framework for calculating the reproduction numbers was applied on the optimized model and their predictions are found to correlate with the true incidence data. In addition, these predictions are compared with a commonly used retrospective method (Wallinga-Teunis) and are found to be in harmony thereby est (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Subramanian Ramakrishnan Ph.D. (Committee Member); Shelley Ehrlich M.D. (Committee Member); Derek Wolf Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science (M.S.), University of Dayton, 2020, Electro-Optics

This work explores new capabilities of recently emerged adaptive multi-beam laser power sources to optimally shape laser power spatial distribution at powder material during metallic laser powder bed fusion additive manufacturing. Conventional laser additive manufacturing (LAM) systems use a highly localized laser beam for powder material melting resulting in strong temperature gradients inside the heat affected zone (HAZ) leading to formation of columnar material grain structure having highly anisotropic mechanical properties. Beam shaping with multi-beam laser power source provides opportunities for on demand and location-specific altering grain structure from columnar to equiaxed resulting in more isotropic mechanical properties of LAM fabricated parts. In this work we perform numerical simulations of theLAM process using a reduced complexity analytical heat transfer solution in order to optimize multi-beam configurations leading to the desired transitioning from columnar to equiaxed grain morphology. The beam shaping optimization was performed using a stochastic parallel gradient descent optimization of the introduced performance metrics. The results demonstrate the possibility to significantly increase the fraction of equiaxed grains in the solidified powder material using optimal positioning and laser power control of multiple laser focal spots during LAM.

Committee: Mikhail Vorontsov (Committee Chair); Chenlong Zhao (Committee Member); Victor Kulikov (Committee Member) Subjects: Optics

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

The increasing complexity of the Powertrain model with the emerging trends in the hybrid and connected vehicles industry demands new approaches. As an Optimal Control Problem for the Energy Management of these class of vehicles becomes more complicated and larger in size due to addition of several mixed integer (continuous and discrete) states and controls variables in a dynamical system, the currently used offline global optimization techniques such as Dynamic Programming may not find a practical application due to a significantly high computational effort or in some cases, even failing to provide any solution at all. Thus, it becomes important to investigate a substitute optimization-based algorithm that can offer a good scalability in terms of numerical efficiency and computational effort as the Optimization Control Problem (OCP) becomes larger in size. In this thesis, we attempt to explore and solve different sizes of Optimal Energy Management Problems concerned with a Parallel P2 Hybrid Electric Vehicle using DP as well as a new algorithm called Pseudospectral Collocation method or PSC (using CasADi). Due to PSC's promising performance and a possible interface with MATLAB/Simulink as shown in the last chapter, this thesis essentially aims to stimulate researchers' interest even further to explore and solve much complicated and larger Hybrid/Electric Vehicle EMS problems using the proposed methodology.

Committee: Qadeer Ahmed Dr. (Advisor); Giorgio Rizzoni Dr. (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, University of Toledo, 2017, Mathematics

In this paper we study convergence rates using quotient convergence factors and root convergence factors as described by Ortega and Rheinboldt for Hestenes' Gram- Schmidt conjugate direction method without derivatives. We do study this computa- tionally and analytically by comparing this conjugate direction method for minimizing a non quadratic function f with Newton's method for solving rf = 0. This method of Hestenes is dierent from that of Smith, Powell, and Zangwill in its mathematical development. All of these ideas have already been developed and based upon 1952 paper of Hestenes and Siefel, the 1980 book Conjugate Direction Methods in Opti- mization by Hestenes and the 1975 paper by Dennemeyer and Mookini. The primary purpose of this work is to provide details analytically and computationally of what has been done before.

Committee: Ivie Stein Ph.D. (Advisor) Subjects: Mathematics