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Transport Models and Optimization Algorithms for Rechargeable Batteries.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Transport Models and Optimization Algorithms for Rechargeable Batteries./
Author:	Deng, Changyu.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Energy. -
Online resource:	click for full text (PQDT)
ISBN:	9798379565053

Transport Models and Optimization Algorithms for Rechargeable Batteries.
Deng, Changyu.

Transport Models and Optimization Algorithms for Rechargeable Batteries. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of Michigan, 2023.

Includes bibliographical references

Rechargeable batteries, such as lithium-ion and sodium-ion batteries, are gaining much attention due to high demand in electronics, electric vehicles, and energy storage. An essential task of battery research and development is to describe the mechanisms by models and optimize the parameters for best performance. In the dissertation, we focus on improving battery models and developing optimization algorithms. Batteries are complicated systems with numerous properties and parameters. We improved model accuracy via obtaining appropriate parameters. Special effort is made to achieve consistency among different measurement techniques when obtaining the electrode particle diffusivity. It has puzzled the community for a long time that two common techniques yield quite different results. We thoroughly analyzed the error of these methods to explain the discrepancy and provide the conditions for accurate measurement. In contrast to lithium-ion batteries, the research of solid-state battery models is relatively sparse. The physics in solid-state electrolyte is different from liquid electrolyte, thus a continuum model is formulated to describe the transport of lithium ions and electrons in solid-state electrolyte. Our model can directly correlate experiment measurement with physical properties. Battery optimization is challenging due to the high cost and long cycling time to evaluate the performance of a battery. It is then vital to design efficient optimization algorithms targeting those features. Two types of algorithms have been investigated to find the optimal parameters in the models. The first algorithm tries to deal with the long cycling issue. A battery cycling optimization framework leveraging recent advances in machine learning is introduced. It allows parallel cyclers, stops unpromising cycles, and automatically yields new configurations of parameters. We show 75% time reduction in our test. The second algorithm tries to search for electrode patterns with higher energy than conventional uniform electrode. A self-direct online learning method is designed to tackle such high dimensional optimization problems. A deep neural network is used as a surrogate model to replace the calculation from the original battery evaluation. This approach reduces the number of calculations by over two orders of magnitude compared with directly applying heuristic algorithms. The resultant electrode pattern has over 1/5 increase in the energy density. The research in this dissertation provides new insights in modeling and parameterization. New optimization algorithms are also proposed to dramatically reduce the time and cost to search for battery parameters.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379565053Subjects--Topical Terms:

784773
Energy.
Subjects--Index Terms:

Lithium-ion batteryIndex Terms--Genre/Form:

554714
Electronic books.

Transport Models and Optimization Algorithms for Rechargeable Batteries.
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520 $a Rechargeable batteries, such as lithium-ion and sodium-ion batteries, are gaining much attention due to high demand in electronics, electric vehicles, and energy storage. An essential task of battery research and development is to describe the mechanisms by models and optimize the parameters for best performance. In the dissertation, we focus on improving battery models and developing optimization algorithms. Batteries are complicated systems with numerous properties and parameters. We improved model accuracy via obtaining appropriate parameters. Special effort is made to achieve consistency among different measurement techniques when obtaining the electrode particle diffusivity. It has puzzled the community for a long time that two common techniques yield quite different results. We thoroughly analyzed the error of these methods to explain the discrepancy and provide the conditions for accurate measurement. In contrast to lithium-ion batteries, the research of solid-state battery models is relatively sparse. The physics in solid-state electrolyte is different from liquid electrolyte, thus a continuum model is formulated to describe the transport of lithium ions and electrons in solid-state electrolyte. Our model can directly correlate experiment measurement with physical properties. Battery optimization is challenging due to the high cost and long cycling time to evaluate the performance of a battery. It is then vital to design efficient optimization algorithms targeting those features. Two types of algorithms have been investigated to find the optimal parameters in the models. The first algorithm tries to deal with the long cycling issue. A battery cycling optimization framework leveraging recent advances in machine learning is introduced. It allows parallel cyclers, stops unpromising cycles, and automatically yields new configurations of parameters. We show 75% time reduction in our test. The second algorithm tries to search for electrode patterns with higher energy than conventional uniform electrode. A self-direct online learning method is designed to tackle such high dimensional optimization problems. A deep neural network is used as a surrogate model to replace the calculation from the original battery evaluation. This approach reduces the number of calculations by over two orders of magnitude compared with directly applying heuristic algorithms. The resultant electrode pattern has over 1/5 increase in the energy density. The research in this dissertation provides new insights in modeling and parameterization. New optimization algorithms are also proposed to dramatically reduce the time and cost to search for battery parameters.
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