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Reliability-Based Design Optimizatio...

Vaidya, Shreyas P.

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Reliability-Based Design Optimization of Battery Thermal Management Systems Under Uncertainty.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reliability-Based Design Optimization of Battery Thermal Management Systems Under Uncertainty./
作者:	Vaidya, Shreyas P.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Alternative energy. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30572996
ISBN:	9798380183826

Reliability-Based Design Optimization of Battery Thermal Management Systems Under Uncertainty.
Vaidya, Shreyas P.

Reliability-Based Design Optimization of Battery Thermal Management Systems Under Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 48 p.

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.S.)--Michigan Technological University, 2023.

This item must not be sold to any third party vendors.

Global warming has led to increased research in renewable energy and the need for efficient energy storage systems. Lithium-ion batteries are a promising solution, but their performance degrades at high temperatures. To improve thermal management, researchers are exploring the use of phase change materials (PCMs) combined with fin structures. Different fin geometries impact heat dissipation. The goal of this study is to perform a reliability-based design optimization of a battery thermal management system for a desired reliability and temperature level. The design geometry consists of four components that include the lithium-ion cell at the core having a fin structure with a PCM module attached to it, and an acrylic shell on the outside. The geometric design variables include the dimension of the outer radius of the battery shell (overall diameter of the battery) and three dimensions of a T-shaped fin structure. Along with the four design variables, two uncertainty parameters of battery heat generation that happens at the core and the ambient convective heat transfer coefficient on the outer surface are considered for the reliability based design optimization. Latin Hypercube Sampling is used to generate sample points for thermal analysis that is done using ANSYS Mechanical APDL. These data points are used to train a machine learning model to predict temperatures for unknown design samples during the optimization process. The optimization is done using a type of an evolutionary algorithm. Initially the optimization problem was formulated using a single objective function that was minimized to find the optimal design configuration. The results of this optimization encouraged to pursue the possibility of multiple optimal solutions and formulate a multi-objective optimization problem.

ISBN: 9798380183826Subjects--Topical Terms:

3436775
Alternative energy.
Subjects--Index Terms:

Global warming

Reliability-Based Design Optimization of Battery Thermal Management Systems Under Uncertainty.
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520 $a Global warming has led to increased research in renewable energy and the need for efficient energy storage systems. Lithium-ion batteries are a promising solution, but their performance degrades at high temperatures. To improve thermal management, researchers are exploring the use of phase change materials (PCMs) combined with fin structures. Different fin geometries impact heat dissipation. The goal of this study is to perform a reliability-based design optimization of a battery thermal management system for a desired reliability and temperature level. The design geometry consists of four components that include the lithium-ion cell at the core having a fin structure with a PCM module attached to it, and an acrylic shell on the outside. The geometric design variables include the dimension of the outer radius of the battery shell (overall diameter of the battery) and three dimensions of a T-shaped fin structure. Along with the four design variables, two uncertainty parameters of battery heat generation that happens at the core and the ambient convective heat transfer coefficient on the outer surface are considered for the reliability based design optimization. Latin Hypercube Sampling is used to generate sample points for thermal analysis that is done using ANSYS Mechanical APDL. These data points are used to train a machine learning model to predict temperatures for unknown design samples during the optimization process. The optimization is done using a type of an evolutionary algorithm. Initially the optimization problem was formulated using a single objective function that was minimized to find the optimal design configuration. The results of this optimization encouraged to pursue the possibility of multiple optimal solutions and formulate a multi-objective optimization problem.
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