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Modeling and Design of Lithium-Ion Batteries: Mechanics and Electrochemistry
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Modeling and Design of Lithium-Ion Batteries: Mechanics and Electrochemistry. |
| 개인저자 | Wu, Bin. |
| 단체저자명 | University of Michigan. Mechanical Engineering. |
| 발행사항 | [S.l.]: University of Michigan., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 155 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085654074 |
| 학위논문주기 | Thesis (Ph.D.)--University of Michigan, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Lu, Wei. |
| 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 | The active materials of lithium-ion batteries exhibit volumetric deformation during lithium intercalation and de-intercalation. Stress stemming from this volume change affects not only the durability of the batteries, but also the electrochemical processes in the electrode. This dissertation focuses on the mechanical and electrochemical modeling and design of lithium-ion batteries, ranging from particle scale to electrode scale.Many electrode materials for lithium-ion battery applications are composed of secondary particles. Such an active material particle is not a solid particle, but consists of many fine primary particles. A mechanical and electrochemical coupled model is developed to simulate the intercalation-induced stress in a secondary particle with the agglomerate structure. In this model the electrochemical and transport processes are accounted for at both the secondary and primary particle levels. For mechanical analysis the secondary particle is treated as a continuum with stress calculated through lithium concentration and elastic deformation. Several important factors that affect stresses in secondary particles are revealed with this model.Active particles with a core-shell structure exhibit superior physical, electrochemical, and mechanical properties over their single-component counterparts in electrodes. A physically rigorous model is developed to describe the diffusion and stress inside the core-shell structure based on a generalized chemical potential. Including both chemical and mechanical effects, the generalized chemical potential governs the diffusion in both the shell and the core. The stress is calculated using the lithium concentration profile. As revealed by the simulations, the core-shell interface is prone to debonding for particles with a thick shell, while shell fracture is more likely to occur for particles with a large core and a relatively thin shell. Based on the simulation results, a design map of the core and shell sizes is generated to avoid both shell fracture and core-shell debonding.As an inherent multiscale structure, a continuum scale battery electrode is composed of many microscale particles. A multiscale model is developed to couple mechanics and electrochemistry consistently at the microscopic and continuum scales. The microscopic particle stress is treated as a superposition of the intra-particle concentration gradient-induced stress and the particle interaction stress, with the latter being related to the continuum scale stress through a representative volume element. Solid diffusion and charge transfer kinetics are generalized with the mechanical effect. In a parallel effort, a direct three-dimensional particle network model is developed to serve as a standard. Comparison of results from the multiscale model and from the particle network model shows that the multiscale model gives good, satisfying accuracy with dramatically reduced computational cost.Simulation-based battery design encounters the difficulty of high computational cost. A systematic approach based on the artificial neural network is developed to reduce the computational burden of simulation based battery design. Two neural networks are constructed using the finite element simulation results from a thermo-electrochemical model. The first neural network serves as a classifier to predict whether a set of input variables is physically feasible. The second neural network yields specific energy and specific power. With a global sensitivity analysis using the neural networks, the effects of input variables on specific energy and specific power are quantified, which is computationally prohibitive for finite element simulations. A design map is generated to fulfill the requirements of both specific energy and specific power. |
| 일반주제명 | Mechanics. Chemical engineering. Mechanical engineering. |
| 언어 | 영어 |
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