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Lithium-Ion Battery Formation Modeling and Diagnostics.
Lithium-Ion Battery Formation Modeling and Diagnostics.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017162833
- International Standard Book Number
- 9798382739731
- Dewey Decimal Classification Number
- 621
- Main Entry-Personal Name
- Weng, Andrew.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of Michigan., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 289 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- General Note
- Advisor: Stefanopoulou, Anna.
- Dissertation Note
- Thesis (Ph.D.)--University of Michigan, 2024.
- Summary, Etc.
- 요약The world needs to transition to sustainable energy as quickly as possible. A cornerstone of this transition is a fully electrified transportation sector which will require the production of automotive-grade lithium-ion batteries at a massive scale. Among the many challenges of battery production, the battery formation step, the last step in battery manufacturing, is both paramount and problematic. It is paramount since all batteries undergo the formation process to build a resilient solid electrolyte interphase (SEI) and to screen for defects. It is problematic because the formation process is expensive to operate, is a major source of factory energy demand, requires larger factory footprints, and takes an order of magnitude longer than nearly every other manufacturing step. Despite the centrality of the formation process in battery manufacturing, steps taken to optimize formation protocols remain ad hoc in the absence of fundamental design principles and physical models. In this thesis, we develop models and methods to enable advances in battery formation protocol design, battery manufacturing process control, and battery lifetime prediction. The work begins by introducing a physics-based electrochemical model of the battery formation process which, for the first time, bridges the gap between the electrochemistry of SEI formation and full-cell performance metrics. We demonstrate that the model can predict emergent system properties such as SEI passivation and cell aging. Using the model, we also verified that faster formation protocols are achievable without compromising battery lifetime. Next, we take a data-driven approach to studying the battery formation process through the lens of scalable diagnostic features, or ``electrochemical fingerprints.'' We show that these diagnostic features can be used to improve battery manufacturing process control and for predicting the impact of formation protocols on battery lifetime immediately after manufacturing. However, great care is needed to ensure reproducible data collection. Finally, the thesis ends by investigating the question of ``how much variability is too much,'' i.e. how much process control is really in battery manufacturing? We demonstrate that, when dissimilar battery cells are cycled in a parallel configuration, the degradation trajectory of individual cells may converge, suggesting that some amount of variability in cell properties at the beginning of life may be tolerated.
- Subject Added Entry-Topical Term
- Energy.
- Subject Added Entry-Topical Term
- Chemical engineering.
- Subject Added Entry-Topical Term
- Mechanical engineering.
- Subject Added Entry-Topical Term
- Physical chemistry.
- Index Term-Uncontrolled
- Battery manufacturing
- Index Term-Uncontrolled
- Electrochemical modeling
- Index Term-Uncontrolled
- Lithium-ion batteries
- Index Term-Uncontrolled
- Battery formation
- Index Term-Uncontrolled
- Battery lifetime prediction
- Index Term-Uncontrolled
- SEI growth
- Added Entry-Corporate Name
- University of Michigan Mechanical Engineering
- Host Item Entry
- Dissertations Abstracts International. 85-12B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:657774
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aWeng, Andrew.
■24510▼aLithium-Ion Battery Formation Modeling and Diagnostics.
■260 ▼a[S.l.]▼bUniversity of Michigan. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a289 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Stefanopoulou, Anna.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aThe world needs to transition to sustainable energy as quickly as possible. A cornerstone of this transition is a fully electrified transportation sector which will require the production of automotive-grade lithium-ion batteries at a massive scale. Among the many challenges of battery production, the battery formation step, the last step in battery manufacturing, is both paramount and problematic. It is paramount since all batteries undergo the formation process to build a resilient solid electrolyte interphase (SEI) and to screen for defects. It is problematic because the formation process is expensive to operate, is a major source of factory energy demand, requires larger factory footprints, and takes an order of magnitude longer than nearly every other manufacturing step. Despite the centrality of the formation process in battery manufacturing, steps taken to optimize formation protocols remain ad hoc in the absence of fundamental design principles and physical models. In this thesis, we develop models and methods to enable advances in battery formation protocol design, battery manufacturing process control, and battery lifetime prediction. The work begins by introducing a physics-based electrochemical model of the battery formation process which, for the first time, bridges the gap between the electrochemistry of SEI formation and full-cell performance metrics. We demonstrate that the model can predict emergent system properties such as SEI passivation and cell aging. Using the model, we also verified that faster formation protocols are achievable without compromising battery lifetime. Next, we take a data-driven approach to studying the battery formation process through the lens of scalable diagnostic features, or ``electrochemical fingerprints.'' We show that these diagnostic features can be used to improve battery manufacturing process control and for predicting the impact of formation protocols on battery lifetime immediately after manufacturing. However, great care is needed to ensure reproducible data collection. Finally, the thesis ends by investigating the question of ``how much variability is too much,'' i.e. how much process control is really in battery manufacturing? We demonstrate that, when dissimilar battery cells are cycled in a parallel configuration, the degradation trajectory of individual cells may converge, suggesting that some amount of variability in cell properties at the beginning of life may be tolerated.
■590 ▼aSchool code: 0127.
■650 4▼aEnergy.
■650 4▼aChemical engineering.
■650 4▼aMechanical engineering.
■650 4▼aPhysical chemistry.
■653 ▼aBattery manufacturing
■653 ▼aElectrochemical modeling
■653 ▼aLithium-ion batteries
■653 ▼aBattery formation
■653 ▼aBattery lifetime prediction
■653 ▼aSEI growth
■690 ▼a0548
■690 ▼a0542
■690 ▼a0791
■690 ▼a0494
■71020▼aUniversity of Michigan▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162833▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
