Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling
A coupled finite elements method (FEM) and machine learning (ML) workflow is presented to optimize the rate capability of thick positive electrodes (ca. 150 μm and 8 mAh/cm2). An ML model is trained based on the geometrical observables of individual LiNi0.8Mn0.1Co0.1O2 particles and their average st...
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| Published in | ACS energy letters Vol. 9; no. 4; pp. 1480 - 1486 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
American Chemical Society
12.04.2024
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| Online Access | Get full text |
| ISSN | 2380-8195 2380-8195 |
| DOI | 10.1021/acsenergylett.4c00203 |
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| Abstract | A coupled finite elements method (FEM) and machine learning (ML) workflow is presented to optimize the rate capability of thick positive electrodes (ca. 150 μm and 8 mAh/cm2). An ML model is trained based on the geometrical observables of individual LiNi0.8Mn0.1Co0.1O2 particles and their average state of discharge (SOD) predicted from FEM modeling. This model not only bypasses lengthy FEM simulations but also provides deeper insights on the importance of pore tortuosity and the active particle size, identified as the limiting phenomenon during the discharge. Based on these findings, a bilayer configuration is proposed to tackle the identified limiting factors for the rate capability. The benefits of this structured electrode are validated through FEM by comparing its performance to a pristine monolayer electrode. Finally, experimental validation using dry processing demonstrates a 40% higher volumetric capacity of the bilayer electrode when compared to the previously reported thick NMC electrodes. |
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| AbstractList | A coupled finite elements method (FEM) and machine learning (ML) workflow is presented to optimize the rate capability of thick positive electrodes (ca. 150 μm and 8 mAh/cm2). An ML model is trained based on the geometrical observables of individual LiNi0.8Mn0.1Co0.1O2 particles and their average state of discharge (SOD) predicted from FEM modeling. This model not only bypasses lengthy FEM simulations but also provides deeper insights on the importance of pore tortuosity and the active particle size, identified as the limiting phenomenon during the discharge. Based on these findings, a bilayer configuration is proposed to tackle the identified limiting factors for the rate capability. The benefits of this structured electrode are validated through FEM by comparing its performance to a pristine monolayer electrode. Finally, experimental validation using dry processing demonstrates a 40% higher volumetric capacity of the bilayer electrode when compared to the previously reported thick NMC electrodes. |
| Author | Meng, Ying Shirley Zhang, Minghao Yao, Weiliang Chouchane, Mehdi Cronk, Ashley |
| AuthorAffiliation | Pritzker School of Molecular Engineering University of Chicago University of California San Diego Materials Science and Engineering Department of NanoEngineering |
| AuthorAffiliation_xml | – name: Materials Science and Engineering – name: University of California San Diego – name: Pritzker School of Molecular Engineering – name: Department of NanoEngineering – name: University of Chicago |
| Author_xml | – sequence: 1 givenname: Mehdi surname: Chouchane fullname: Chouchane, Mehdi organization: University of Chicago – sequence: 2 givenname: Weiliang surname: Yao fullname: Yao, Weiliang organization: Materials Science and Engineering – sequence: 3 givenname: Ashley orcidid: 0000-0001-6147-4662 surname: Cronk fullname: Cronk, Ashley organization: Materials Science and Engineering – sequence: 4 givenname: Minghao surname: Zhang fullname: Zhang, Minghao email: miz016@eng.ucsd.edu organization: University of California San Diego – sequence: 5 givenname: Ying Shirley orcidid: 0000-0001-8936-8845 surname: Meng fullname: Meng, Ying Shirley email: shirleymeng@uchicago.edu organization: University of California San Diego |
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