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Gaussian Process Regression based heat and electrical models for a lithium-ion battery cell
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Title
Gaussian Process Regression based heat and electrical models for a lithium-ion battery cell
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Li
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Chao
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Chao Li
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Assimina A.
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Assimina A. Pelegri
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Jonathan P.
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Jonathan P. Singer
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internal member
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Malhotra
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Rajiv
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Rajiv Malhotra
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Advisory Committee
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internal member
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Shan
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Baoxiang
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Baoxiang Shan
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Advisory Committee
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Rutgers University
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School of Graduate Studies
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theses
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ETD doctoral
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2021
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2021-01
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2021
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English
Abstract
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The imbalance of electrical potential, temperature, and the State of Charge (SOC) within battery cells may cause safety issues in applications. Models that can predict battery cells’ thermal and electrical behaviors become necessary for real-time battery management systems to regulate the imbalance.
This dissertation introduces a Gaussian Process Regression (GPR) based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure developed by (Kim, et al. 2011). The framework predictions can reach high accuracies as the full-order full-distribution simulations based on MSMD. It relies on the pseudo-2D model developed by (Doyle, Fuller and Newman 1993) to generate training data, which shifts computation burdens from real-time battery management systems to lab data preparation.
This dissertation introduces deterministic and uncertain input GPR models in the battery cell’s particle and electrode domains. These models work in different phases of the battery cell potentiostatic discharge simulation, where different types of probabilistic finite element analysis (FEA) procedures are applied correspondingly. Dimensional model representation approximations combined with Gauss-Hermite quadrature methods are employed to give solution distributions of temperatures and plate electrical potential differences in the cell domain.
Testing results illustrate the reliability of the GPR based data-driven framework in accuracy and stability under various circumstances; this was accomplished by comparing our results from other researchers’ methods.
This dissertation introduces a Gaussian Process Regression (GPR) based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure developed by (Kim, et al. 2011). The framework predictions can reach high accuracies as the full-order full-distribution simulations based on MSMD. It relies on the pseudo-2D model developed by (Doyle, Fuller and Newman 1993) to generate training data, which shifts computation burdens from real-time battery management systems to lab data preparation.
This dissertation introduces deterministic and uncertain input GPR models in the battery cell’s particle and electrode domains. These models work in different phases of the battery cell potentiostatic discharge simulation, where different types of probabilistic finite element analysis (FEA) procedures are applied correspondingly. Dimensional model representation approximations combined with Gauss-Hermite quadrature methods are employed to give solution distributions of temperatures and plate electrical potential differences in the cell domain.
Testing results illustrate the reliability of the GPR based data-driven framework in accuracy and stability under various circumstances; this was accomplished by comparing our results from other researchers’ methods.
Subject
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Topic
battery
Subject
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Topic
Lithium-ion batteries
Subject
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Topic
Gaussian processes
Subject
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Topic
Mechanical and Aerospace Engineering
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Rutgers University Electronic Theses and Dissertations
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1 online resource (xi, 96 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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doi:10.7282/t3-9ezt-gq25
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Rights
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The author owns the copyright to this work.
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Li
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Chao
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Permission or license
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2020-12-19 18:40:53
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Chao Li
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Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2021-01-31
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2023-01-31
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Access to this PDF has been restricted at the author's request. It will be publicly available after January 31st, 2023.
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Copyright protected
Availability
Status
Open
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Permission or license
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