期刊
JOULE
卷 5, 期 12, 页码 3187-3203出版社
CELL PRESS
DOI: 10.1016/j.joule.2021.10.010
关键词
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资金
- Toyota Research Institute
- Tsinghua-Toyota Joint Research Fund
A new data-driven methodology is demonstrated for rapid and accurate prediction of cycle life in lithium-ion batteries, enabling exploration of cycling protocol design spaces with significant reduction in testing time. By combining lifetime early prediction with a hierarchical Bayesian model, high protocol-lifetime prediction performance is achieved with minimal testing.
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. 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.
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