期刊
ENERGY STORAGE
卷 1, 期 6, 页码 -出版社
WILEY
DOI: 10.1002/est2.98
关键词
lifetime prediction; lithium-ion battery; machine learning
资金
- National Natural Science Foundation of China [51801135, 51771136]
- Science and Technology Support Program of Tianjin [16ZXCLGX00110, 16ZXCLGX00070]
- China Postdoctoral Science Foundation [2019M650047]
- Independent Innovation Fund of Tianjin University [2019XZY-0021]
This work applies machine learning tools to achieve the early prediction of commercial battery life. We compared the prediction accuracy of different machine learning algorithms to the battery database. Among various algorithms, the decision tree (DT) method exhibits the highest accuracy of 95.2% to predict whether the battery can maintain above 80% initial capacity after 550cycles. Using the initial two cycles of data, DT proposes that the change of discharge capacity is the main feature to estimate the lifetime type of batteries. Given the first 100cycles, the factor with the maximum weight turns to the internal resistance for estimating the battery lifetime.
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