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Simulation-Based optimization of Hybrid Systems Using Derivative Free Optimization Techniques
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Simulation-Based optimization of Hybrid Systems Using Derivative Free Optimization Techniques. |
| 개인저자 | Jayakumar, Adithya. |
| 단체저자명 | The Ohio State University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: The Ohio State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 163 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781687936707 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2018. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Rizzoni, Giorgio. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Performing numerical optimization in large scale simulations environments is complicated by the fact that the overall objective function might be too computationally intensive or impossible to define in its closed form. In these cases, simulation-based optimization algorithms, which do not need the exact closed form objective function are the only viable solution method. Derivative Free Optimization algorithms are one such class of algorithms that does not need the derivative of the objective function in order to find the optimum. They instead use function evaluations to traverse the search space. This dissertation addresses the optimization challenges of large scale simulators that do not lend themselves to gradient based optimization.While the field of simulation-based optimization has been in existence for a few decades, the growing complexity of models in recent years puts a focus on the field to provide effective strategies to efficiently perform the required optimization. The difference between simulations and the real world systems they represent is that simulations use assumptions. It is important that these assumptions are within an acceptable tolerance which enable them to model reality with an appropriate level of certainty, within a reasonable amount of time, and using limited computational resources. Simulators use various ways to simplify reality and one way this is done is through the use of look-up tables (LUT). A look up table is an matrix that enables complicated computation to be replaced with relatively simpler array indexing. Finding optimal solutions to simulators which use LUTs is complicated by LUTs being discrete and event based. In addition, most simulation models that are used to model decision making mechanisms such as embedded control systems consist of both discrete and continuous state dynamics. These hybrid system models need both the discrete and continuous state dynamics to be analyzed and optimized simultaneously. This dissertation addresses a methodology that can be employed to optimize hybrid systems that use LUTs in simulation-based environments. The particular simulator and application addressed here is the optimization of fuel economy in hybrid electric vehicles (HEVs). Accurately estimating the energy consumption of hybrid electric vehicles is complicated by the fact that these vehicles have multiple power sources and complex control strategies. As a starting point of this research, to ensure that available vehicle simulators can be validated, a thorough literature review of energy consumption in HEVs was done both on a component and an overall level. This then allowed model validation to be performed. New methods of model validation for the case of vehicle simulators were also developed and are discussed in this dissertation. Also in this document, the optimization framework developed to robustly minimize fuel economy in a hybrid electric vehicle simulator is discussed. Since the vehicle simulator is a hybrid system using LUTs, the methodology developed here will be applicable in many simulation optimization environments. |
| 일반주제명 | Computer engineering. Electrical engineering. Mechanical engineering. |
| 언어 | 영어 |
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