期刊
ISCIENCE
卷 25, 期 5, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.isci.2022.104260
关键词
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资金
- National Natural Science Foundation of China [52102420]
- China Postdoctoral Science Foundation [2021M693725]
- Chongqing Natural Science Foundation [cstc2020jcyj-bshX0079, cstc2021jcyj-jqX0001]
Accurately evaluating the health status of lithium-ion batteries is crucial for improving their safety, efficiency, and economy. In this study, three data-driven methods were developed to estimate battery state of health (SOH) using a short random charging segment. Experimental results demonstrated that the models achieved high-precision SOH estimation under different conditions.
Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.
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