Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science
Abstract
Numerical physics-based models are useful in understanding battery performance and devel- oping optimal battery design architectures. Data science developments have enabled software algorithms to perform data analysis and decision making that traditionally could only be per- formed by technical experts. Traditional workflows of model development - manual parameter estimation through visual comparison of simulations to experimental observations, and model discrimination through expert intuition - are time-consuming, and difficult to justify. This paper compares the conclusions derived from computationally scalable algorithms to conclu- sions developed by expert researchers. This paper illustrates how data science techniques, such as cross-validation and lasso regression, can be used to augment physics-based simulations to perform data analysis such as parameter estimation, model selection, variable selection, and model-guided design of experiment. The validation of these algorithms is that they produce results similar to those of the expert modeler. The algorithms outlined are well-established and the approaches are general, so can be applied to a variety of battery chemistries and architectures. The conclusions reached using these approaches are in agreement with expert analysis (literature results), were reached with minimal human intervention, and provide quantitative justification. By minimizing the amount of expert time, and providing rigorous quantitative justifications, thesemore »
- Publication Date:
- Research Org.:
- Energy Frontier Research Centers (EFRC) (United States). Center for Mesoscale Transport Properties (m2mt); State Univ. of New York (SUNY), Syracuse, NY (United States); Stony Brook Univ., NY (United States); Columbia Univ., New York, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Contributing Org.:
- Energy Frontier Research Center. Center for Mesoscale Transport Properties
- OSTI Identifier:
- 1597380
- Grant/Contract Number:
- SC0012673
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of the Electrochemical Society
- Additional Journal Information:
- Journal Volume: 167; Journal Issue: 1; Journal ID: ISSN 0013-4651
- Publisher:
- IOP Publishing - The Electrochemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 25 ENERGY STORAGE
Citation Formats
Brady, Nicholas W., Gould, Christian Alexander, and West, Alan C. Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science. United States: N. p., 2019.
Web. doi:10.1149/2.0012001JES.
Brady, Nicholas W., Gould, Christian Alexander, & West, Alan C. Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science. United States. https://doi.org/10.1149/2.0012001JES
Brady, Nicholas W., Gould, Christian Alexander, and West, Alan C. Fri .
"Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science". United States. https://doi.org/10.1149/2.0012001JES. https://www.osti.gov/servlets/purl/1597380.
@article{osti_1597380,
title = {Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science},
author = {Brady, Nicholas W. and Gould, Christian Alexander and West, Alan C.},
abstractNote = {Numerical physics-based models are useful in understanding battery performance and devel- oping optimal battery design architectures. Data science developments have enabled software algorithms to perform data analysis and decision making that traditionally could only be per- formed by technical experts. Traditional workflows of model development - manual parameter estimation through visual comparison of simulations to experimental observations, and model discrimination through expert intuition - are time-consuming, and difficult to justify. This paper compares the conclusions derived from computationally scalable algorithms to conclu- sions developed by expert researchers. This paper illustrates how data science techniques, such as cross-validation and lasso regression, can be used to augment physics-based simulations to perform data analysis such as parameter estimation, model selection, variable selection, and model-guided design of experiment. The validation of these algorithms is that they produce results similar to those of the expert modeler. The algorithms outlined are well-established and the approaches are general, so can be applied to a variety of battery chemistries and architectures. The conclusions reached using these approaches are in agreement with expert analysis (literature results), were reached with minimal human intervention, and provide quantitative justification. By minimizing the amount of expert time, and providing rigorous quantitative justifications, these methods may accelerate battery development.},
doi = {10.1149/2.0012001JES},
journal = {Journal of the Electrochemical Society},
number = 1,
volume = 167,
place = {United States},
year = {2019},
month = {8}
}
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