Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/app11104671
Keywords
machine learning; remaining useful life; symbolic regression
Categories
Funding
- National Natural Science Foundation of China [52077096]
- Hubei Provincial Science and Technology Major Project of China [2020AEA011]
- Key Research & Development Plan of Hubei Province of China [2020BAB100]
- excellent youthful project Scientific research of Hunan Provincial Department of Education [20B491]
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This study demonstrates the potential of data-driven machine learning algorithms in predicting solid-state battery lifetimes, showing successful predictions with symbolic regression and providing a new approach for battery classification, utilization, and recycling.
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.
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