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Title: Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

Abstract

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed based on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance ofmore » Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.« less

Authors:
 [1];  [2];  [2];  [2]
+ Show Author Affiliations
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1422334
Report Number(s):
PNNL-SA-123847
Journal ID: ISSN 0013-4651; KJ0401000
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the Electrochemical Society
Additional Journal Information:
Journal Volume: 164; Journal Issue: 11; Journal ID: ISSN 0013-4651
Publisher:
The Electrochemical Society
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE

Citation Formats

Pan, Wenxiao, Yang, Xiu, Bao, Jie, and Wang, Michelle. Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization. United States: N. p., 2017. Web. doi:10.1149/2.0511711jes.
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Pan, Wenxiao, Yang, Xiu, Bao, Jie, & Wang, Michelle. Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization. United States. https://doi.org/10.1149/2.0511711jes
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Pan, Wenxiao, Yang, Xiu, Bao, Jie, and Wang, Michelle. Sun . "Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization". United States. https://doi.org/10.1149/2.0511711jes. https://www.osti.gov/servlets/purl/1422334.
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@article{osti_1422334,
title = {Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization},
author = {Pan, Wenxiao and Yang, Xiu and Bao, Jie and Wang, Michelle},
abstractNote = {We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed based on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.},
doi = {10.1149/2.0511711jes},
journal = {Journal of the Electrochemical Society},
number = 11,
volume = 164,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2017},
month = {Sun Jan 01 00:00:00 EST 2017}
}
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https://doi.org/10.1149/2.0511711jes
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    Works referencing / citing this record:

    Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries
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    • Advanced Theory and Simulations, Vol. 3, Issue 2
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    Three-Dimensional Nitrogen-Doped Hollow Carbon Fiber with a Micro-Scale Diameter as a Binder-Free Oxygen Electrode for Li-O 2 Batteries
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    • Lim, Katie Heeyum; Kweon, Heejun; Kim, Hansung
    • Journal of The Electrochemical Society, Vol. 166, Issue 14
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