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The Role of Data Science in Numerical Modeling of Lithium Ion Batteries
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | The Role of Data Science in Numerical Modeling of Lithium Ion Batteries. |
| 개인저자 | Dawson-Elli, Neal. |
| 단체저자명 | University of Washington. Chemical Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 146 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085716253 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Subramanian, Venkat R. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Batteries are complex electrochemical devices which are nearly ubiquitous in today's society. As the energy demands of mobile devices increase, the performance of batteries must also improve to keep pace. One of the key elements in iterative battery design is the application of numerical models which can predict the properties of potential batteries at significantly reduced cost compared to cell development, enabling high-throughput screening of potential materials and geometries. These models can vary in complexity from simple empirical fits, through continuum-scale models, up to molecular dynamics simulations, which offer increased fidelity, but at an extremely high computational cost.In addition to first-principles models, data-driven models have become popular as the available computational resources and amount of available data have grown astronomically. These models use self-tuning algorithms which form highly accurate nonlinear mappings from inputs to outputs, providing excellent accuracy for relatively low computational cost at runtime.While traditional data-driven models can achieve impressive results given a large amount of data, the acquisition of data at the proper scale is expensive, does not generalize well to other use conditions or battery chemistries, and offers little guidance in the form of physical interpretability. In this work, combinations of physical models and data-driven models are utilized in order to provide highly accurate, flexible applications of the information contained within the first-principles models, while also significantly reducing computational cost at runtime. While design applications of equivalent techniques are conceivable, this work focuses on applications for the calibration of first-principles models for the purposes of improved control of existing electrochemical cells. |
| 일반주제명 | Chemical engineering. Alternative energy. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
