請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91498
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 郭修伯 | zh_TW |
dc.contributor.advisor | Hsiu-Po Kuo | en |
dc.contributor.author | 莊孟融 | zh_TW |
dc.contributor.author | Meng-Jung Chuang | en |
dc.date.accessioned | 2024-01-28T16:16:19Z | - |
dc.date.available | 2024-01-29 | - |
dc.date.copyright | 2024-01-27 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-02 | - |
dc.identifier.citation | 1. Furnas, C., Grading aggregates-I.-Mathematical relations for beds of broken solids of maximum density. Industrial & Engineering Chemistry, 1931. 23(9): p. 1052-1058.
2. Wiącek, J. and M. Stasiak, Effect of the particle size ratio on the structural properties of granular mixtures with discrete particle size distribution. Granular Matter, 2018. 20(2): p. 1-9. 3. Rassouly, S., The packing density ofperfect'binary mixtures. Powder technology, 1999. 103(2): p. 145-150. 4. Kyrylyuk, A.V. and A.P. Philipse, Effect of particle shape on the random packing density of amorphous solids. physica status solidi (a), 2011. 208(10): p. 2299-2302. 5. Zhao, S., et al., Particle shape effects on fabric of granular random packing. Powder technology, 2017. 310: p. 175-186. 6. Donev, A., et al., Improving the density of jammed disordered packings using ellipsoids. Science, 2004. 303(5660): p. 990-993. 7. Delaney, G.W. and P.W. Cleary, The packing properties of superellipsoids. Europhysics Letters, 2010. 89(3): p. 34002. 8. Zhou, Z.-Y., et al., Dynamic simulation of the packing of ellipsoidal particles. Industrial & engineering chemistry research, 2011. 50(16): p. 9787-9798. 9. Lubachevsky, B.D. and F.H. Stillinger, Geometric properties of random disk packings. Journal of statistical Physics, 1990. 60: p. 561-583. 10. Howell, D., Electrochemical Energy Storage R&D Overview. Department of Energy, 2017. 11. Hawley, W.B. and J. Li, Electrode manufacturing for lithium-ion batteries—Analysis of current and next generation processing. Journal of Energy Storage, 2019. 25: p. 100862. 12. Li, J., et al., From materials to cell: state-of-the-art and prospective technologies for lithium-ion battery electrode processing. Chemical Reviews, 2021. 122(1): p. 903-956. 13. Liu, Y., et al., Current and future lithium-ion battery manufacturing. IScience, 2021. 24(4): p. 102332. 14. Malik, M., K.H. Chan, and G. Azimi, Review on the synthesis of LiNixMnyCo1-x-yO2 (NMC) cathodes for lithium-ion batteries. Materials Today Energy, 2022: p. 101066. 15. Huang, H., S.-C. Yin, and L.s. Nazar, Approaching theoretical capacity of LiFePO4 at room temperature at high rates. Electrochemical and solid-state letters, 2001. 4(10): p. A170. 16. Krishna Kumar, S., S. Ghosh, and S.K. Martha, Synergistic effect of magnesium and fluorine doping on the electrochemical performance of lithium-manganese rich (LMR)-based Ni-Mn-Co-oxide (NMC) cathodes for lithium-ion batteries. Ionics, 2017. 23: p. 1655-1662. 17. Busà, C., M. Belekoukia, and M.J. Loveridge, The effects of ambient storage conditions on the structural and electrochemical properties of NMC-811 cathodes for Li-ion batteries. Electrochimica Acta, 2021. 366: p. 137358. 18. Qi, X., et al., Understanding the influence of conductive carbon additives surface area on the rate performance of LiFePO4 cathodes for lithium ion batteries. Carbon, 2013. 64: p. 334-340. 19. Shin, H.C., W.I. Cho, and H. Jang, Electrochemical properties of the carbon-coated LiFePO4 as a cathode material for lithium-ion secondary batteries. Journal of Power Sources, 2006. 159(2): p. 1383-1388. 20. Shi, Y., X. Zhou, and G. Yu, Material and structural design of novel binder systems for high-energy, high-power lithium-ion batteries. Accounts of chemical research, 2017. 50(11): p. 2642-2652. 21. Kang, G.-d. and Y.-m. Cao, Application and modification of poly (vinylidene fluoride)(PVDF) membranes–a review. Journal of membrane science, 2014. 463: p. 145-165. 22. Cundall, P.A. and O.D. Strack, A discrete numerical model for granular assemblies. geotechnique, 1979. 29(1): p. 47-65. 23. Park, J. and N. Kang, Applications of fiber models based on discrete element method to string vibration. Journal of Mechanical Science and Technology, 2009. 23(2): p. 372-380. 24. Rovigatti, L., et al., Connecting elasticity and effective interactions of neutral microgels: The validity of the Hertzian model. Macromolecules, 2019. 52(13): p. 4895-4906. 25. Coetzee, C., Edinburgh-Elasto-Plastic-Adhesion (EEPA) Contact Model. 2020. 26. Morrissey, J.P., S.C. Thakur, and J. Ooi, EDEM contact model: adhesive elasto-plastic model. Granul. Matter, 2014. 16(3): p. 383-400. 27. Schreiner, D., A. Klinger, and G. Reinhart, Modeling of the Calendering Process for Lithium-Ion Batteries with DEM Simulation. Procedia CIRP, 2020. 93: p. 149-155. 28. Meyer, C., et al., Characterization of the calendering process for compaction of electrodes for lithium-ion batteries. Journal of Materials Processing Technology, 2017. 249: p. 172-178. 29. Primo, E.N., et al., Understanding the calendering processability of Li (Ni0. 33Mn0. 33Co0. 33) O2-based cathodes. Journal of Power Sources, 2021. 488: p. 229361. 30. Giménez, C.S., et al., Numerical simulation of the behavior of lithium-ion battery electrodes during the calendaring process via the discrete element method. Powder Technology, 2019. 349: p. 1-11. 31. Diener, A., et al., Evaluation of Deformation Behavior and Fast Elastic Recovery of Lithium‐Ion Battery Cathodes via Direct Roll‐Gap Detection During Calendering. Energy Technology, 2022. 10(4): p. 2101033. 32. Bergman, D.J. and D. Stroud, Physical properties of macroscopically inhomogeneous media, in Solid state physics. 1992, Elsevier. p. 147-269. 33. Froboese, L., et al., Effect of microstructure on the ionic conductivity of an all solid-state battery electrode. Journal of the Electrochemical Society, 2019. 166(2): p. A318. 34. Yan, Z., et al., Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis. Computational Particle Mechanics, 2015. 2(3): p. 283-299. 35. Walton, O.R. and R.L. Braun, Viscosity, granular‐temperature, and stress calculations for shearing assemblies of inelastic, frictional disks. Journal of rheology, 1986. 30(5): p. 949-980. 36. Schreiner, D., et al., DEM Simulations of the Calendering Process: Parameterization of the Electrode Material of Lithium-Ion Batteries. Procedia CIRP, 2021. 104: p. 91-97. 37. Chen, D., et al., Percolation theory to predict effective properties of solid oxide fuel-cell composite electrodes. Journal of Power Sources, 2009. 191(2): p. 240-252. 38. Sangrós Giménez, C., et al., Modeling the Electrical Conductive Paths within All‐Solid‐State Battery Electrodes. Chemical Engineering & Technology, 2020. 43(5): p. 819-829. 39. Jeon, D.H., J.H. Nam, and C.-J. Kim, Microstructural optimization of anode-supported solid oxide fuel cells by a comprehensive microscale model. Journal of The Electrochemical Society, 2006. 153(2): p. A406. 40. Ebner, M. and V. Wood, Tool for tortuosity estimation in lithium ion battery porous electrodes. Journal of The Electrochemical Society, 2014. 162(2): p. A3064. 41. Froboese, L., et al., Mercury intrusion for ion-and conversion-based battery electrodes–structure and diffusion coefficient determination. Materials Characterization, 2017. 133: p. 102-111. 42. Shen, L. and Z. Chen, Critical review of the impact of tortuosity on diffusion. Chemical Engineering Science, 2007. 62(14): p. 3748-3755. 43. Kondo, H., et al., Influence of the active material on the electronic conductivity of the positive electrode in lithium-ion batteries. Journal of The Electrochemical Society, 2019. 166(8): p. A1285. 44. Chen, Y.-H., et al., Selection of conductive additives in li-ion battery cathodes: A numerical study. Journal of the Electrochemical Society, 2007. 154(10): p. A978. 45. Merzouki, A. and N. Haddaoui, Electrical conductivity modeling of polypropylene composites filled with carbon black and acetylene black. International Scholarly Research Notices, 2012. 2012. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91498 | - |
dc.description.abstract | 本研究透過離散元素法DEM分析鋰電池電極顆粒堆積行為,並藉由分析電極堆積以及經過壓延程序的模擬結果探討不同顆粒組成比例、粒徑分布以及顆粒形狀時堆積密度以及導電效能的差異。透過Altair® EDEM™軟體模擬電極正極的壓延程序,透過數值分析探討不同電極條件下的效能。由結果可得,當NMC顆粒為橢球時有較大的堆積密度,最大可達0.57,且不同體積時的橢球可達到最大密度;而改變組成比例當碳黑為2wt%至6wt%時,可以發現當碳黑的比例升高時,會造成堆積密度下降,但電子導電度上升,最高可達4.35×10-2,原因在於碳黑所佔體積比例增加,藉由分析碳黑以及PVDF的組成比例,發現兩者在比值1.5至2.0之間有較佳的堆積密度,最大可達約0.57;改變粒徑分布時,堆積密度受到的影響較小,但在NMC顆粒標準差為0.02時,有較大的導電度,可達約7.78×10-2,此時雖然堆積密度以及碳黑所佔體積比例較接近,但碳黑配位數較大;改變碳黑尺寸,發現碳黑在小尺寸時,堆積密度較小,但導電度較大,最高可達1.21×10-2,由於在固定重量組成比例時,小尺寸碳黑顆粒有較大的體積分率。此外,本研究分析電極壓延後的彈性恢復曲線,分別施以150MPa、300MPa、600MPa以及1200MPa,發現當正向壓延力較大時,電極密度最大可達0.74,但回彈(Spring back)的比例較大。而在壓延過程中,為了確保電極的輾壓無各向異性,透過應力張量以及結構張量的計算來分析對角向XX、YY以及ZZ,由結果可以發現,應力張量計算結果在XX、YY向數值相近,而在ZZ方向較大,顯示電極有達到近完整且平均的輾壓,且透過結構張量的分析確保顆粒在各向所受力皆隨著壓延正向力增加而上升,且XX以及YY向數值接近,表示在平面的受力平均。 | zh_TW |
dc.description.abstract | This study utilized the discrete element method (DEM) to analyze the particle packing behavior of lithium-ion battery electrodes. By analyzing the electrode packing and simulating the calendaring process, the study explored the differences in packing density and conductivity performance based on various particle composition ratios, particle size distributions, and particle shapes. The Altair® EDEM™ software was used to simulate the rolling process of the positive electrode, and numerical analysis was conducted to investigate the performance under different electrode conditions.
The results showed that when the NMC particles were ellipsoidal, a higher packing density was achieved, with a maximum of 0.57. Additionally, ellipsoids with different volumes exhibited larger packing densities. When the composition ratio of carbon black increased from 2wt% to 6wt%, it was observed that the packing density decreased while the electronic conductivity increased, reaching a maximum of 4.35×10-2. This was because the volume fraction of carbon black increased. By analyzing the composition ratio of carbon black and PVDF, it was found that the best packing density was achieved when the ratio was between 1.5 and 2.0, with a maximum of approximately 0.57. Changing the particle size distribution had a relatively small impact on the packing density. However, when the standard deviation of NMC particles was 0.02, a higher conductivity was obtained, reaching approximately 7.78×10-2. At this point, although the packing density and the volume fraction of carbon black were closer, the coordination number of carbon black was larger. When changing the size of carbon black, it was found that smaller carbon black particles resulted in a lower packing density but higher conductivity, with a maximum of 1.21×10-2. This was because smaller carbon black particles had a larger volume fraction under a fixed weight composition ratio. Furthermore, the study analyzed the elastic recovery curve after electrode calendaring with applied pressures of 150MPa, 300MPa, 600MPa, and 1200MPa. It was found that a higher normal compacting force resulted in a maximum electrode density of 0.74 but with a larger spring back proportion. During the calendaring process, in order to ensure isotropy of the electrode calendaring, stress tensor and fabric tensor calculations were used to analyze the diagonal directions XX, YY, and ZZ. The results showed that the stress tensor calculations were similar in the XX and YY directions, while larger in the ZZ direction, indicating that the electrode achieved near-complete and uniform compacting. The analysis of the fabric tensor ensured that the forces acting on the particles in all directions increased with the increasing compacting force, with similar values in the XX and YY directions, indicating an average distribution of forces in the plane. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-28T16:16:19Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-01-28T16:16:19Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 目錄 I
圖目錄 IV 表目錄 VIII 第一章 緒論 1 第二章 文獻回顧 2 2.1 顆粒堆積密度(Particle Packing Density) 2 2.1.1 粒徑比與粒徑分佈組成 2 2.1.2 顆粒形狀 5 2.2 鋰電池(Lithium-ion Battery) 9 2.3 離散元素法 (Discrete Element Method) 13 2.4 壓延(Calendaring) 16 2.5 模擬壓延程序後效能分析(Post-Calendared Simulation Analysis) 22 第三章 研究方法 25 3.1 離散元素法模擬應用 25 3.2 電極性質 26 3.2.1電極材料顆粒及其性質 26 3.2.2 電極材料組成比例 27 3.2.3 活性物質形狀 27 3.2.4 電極材料粒徑分佈 28 3.2.5 正向力大小 28 3.3 EDEM模擬及參數設定 29 3.3.1 電極材料與裝置材料的性質設定 29 3.3.2 電極材料物理交互作用設定 30 3.3.3 EEPA模型參數設定 35 3.3.4顆粒形狀設定 38 3.3.5時間步設定 39 3.3.6 模擬網格大小 40 3.3.7 模擬結果輸出 40 3.4 電極壓延模擬過程 41 3.5 電子導電度與電滲流理論應用 45 3.6 離子導電度分析 47 3.7 壓延後電極分析 50 3.7.1 電極彈性恢復 50 3.7.2 電極張量分析 50 第四章 結果與討論 52 4.1 顆粒形狀與堆積密度及導電度之關係 52 4.1.1 不同形狀且不同體積活型物質 52 4.1.2 不同形狀且同體積活型物質 57 4.2 粒徑分布與堆積密度及導電度關係 62 4.3 組成比例與堆積密度及導電度關係 74 4.3.1 固定PVDF比例組成與密度及導電之間關係 74 4.3.2 同時改變組成比例與堆積密度及導電度之間的關係 88 4.3.3 CB/PVDF重量比與導電度之間的關係 93 4.4碳黑顆粒大小與堆積密度及導電度關係 101 4.5綜合效能分析 104 4.5.1 不同NMC顆粒形狀、碳黑尺寸以及碳黑比例之堆積分析 105 4.5.2 不同NMC顆粒形狀、碳黑尺寸及NMC顆粒標準差之堆積分析 107 4.5.3 不同碳黑尺寸、NMC顆粒標準差以及碳黑比例之堆積分析 109 4.5.4 不同NMC顆粒形狀、NMC粒徑分布以及碳黑組成比例之堆積分析 111 4.6 電極彈性恢復 113 4.7 電極張量分析 119 4.7.1 應力張量 119 4.7.2結構張量 120 第五章 結論 121 參考文獻 123 | - |
dc.language.iso | zh_TW | - |
dc.title | 以離散元素法設計鋰電池正極之最密堆積及其效能評估 | zh_TW |
dc.title | Design of Densest Packing in Li-ion Battery Cathode and its Performance Evaluation by Discrete Element Method | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 許瑞祺;陳嘉晉 | zh_TW |
dc.contributor.oralexamcommittee | Ruey-Chi Hsu;Chia-Chin Chen | en |
dc.subject.keyword | 離散元素法,鋰電池,壓延,堆積密度,導電度,彈性恢復,張量, | zh_TW |
dc.subject.keyword | DEM,Li-ion battery,Calendaring,Packing density,Conductivity,Elastic recovery,Tensor, | en |
dc.relation.page | 126 | - |
dc.identifier.doi | 10.6342/NTU202302729 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-07 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 化學工程學系 | - |
顯示於系所單位: | 化學工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 6.93 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。