Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
Abstract
Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.
- Authors:
-
- Tsinghua University, Beijing (China)
- Stanford University, CA (United States)
- Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
- Toyota Research Institute, Los Altos, CA (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office. Batteries for Advanced Transportation Technologies (BATT) Program; Toyota Research Institute
- OSTI Identifier:
- 1906461
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Joule
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 12; Journal ID: ISSN 2542-4351
- Publisher:
- Elsevier - Cell Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 25 ENERGY STORAGE; lithium-ion batteries; Bayesian learning; energy storage systems; cycling protocols; data-driven prediction; machine learning; cycle life prediction
Citation Formats
Jiang, Benben, Gent, William E., Mohr, Fabian, Das, Supratim, Berliner, Marc D., Forsuelo, Michael, Zhao, Hongbo, Attia, Peter M., Grover, Aditya, Herring, Patrick K., Bazant, Martin Z., Harris, Stephen J., Ermon, Stefano, Chueh, William C., and Braatz, Richard D. Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols. United States: N. p., 2021.
Web. doi:10.1016/j.joule.2021.10.010.
Jiang, Benben, Gent, William E., Mohr, Fabian, Das, Supratim, Berliner, Marc D., Forsuelo, Michael, Zhao, Hongbo, Attia, Peter M., Grover, Aditya, Herring, Patrick K., Bazant, Martin Z., Harris, Stephen J., Ermon, Stefano, Chueh, William C., & Braatz, Richard D. Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols. United States. https://doi.org/10.1016/j.joule.2021.10.010
Jiang, Benben, Gent, William E., Mohr, Fabian, Das, Supratim, Berliner, Marc D., Forsuelo, Michael, Zhao, Hongbo, Attia, Peter M., Grover, Aditya, Herring, Patrick K., Bazant, Martin Z., Harris, Stephen J., Ermon, Stefano, Chueh, William C., and Braatz, Richard D. Fri .
"Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols". United States. https://doi.org/10.1016/j.joule.2021.10.010. https://www.osti.gov/servlets/purl/1906461.
@article{osti_1906461,
title = {Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols},
author = {Jiang, Benben and Gent, William E. and Mohr, Fabian and Das, Supratim and Berliner, Marc D. and Forsuelo, Michael and Zhao, Hongbo and Attia, Peter M. and Grover, Aditya and Herring, Patrick K. and Bazant, Martin Z. and Harris, Stephen J. and Ermon, Stefano and Chueh, William C. and Braatz, Richard D.},
abstractNote = {Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.},
doi = {10.1016/j.joule.2021.10.010},
journal = {Joule},
number = 12,
volume = 5,
place = {United States},
year = {Fri Oct 29 00:00:00 EDT 2021},
month = {Fri Oct 29 00:00:00 EDT 2021}
}
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