Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 5, Pages 2965-2973Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3106593
Keywords
Feature extraction; Batteries; Degradation; Temperature measurement; Voltage measurement; Time measurement; Data mining; Battery; degradation; feature selection; lithium-ion; machine learning
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Funding
- Engineering and Physical Sciences Research Council (EPSRC)
- Siemens
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This article proposes a data-driven method that uses automated feature selection to predict the degradation trend of lithium-ion batteries, including capacity fade trajectory, knee point, and end of life. The study found that calendar time and time spent in specific voltage regions have a significant impact on battery degradation.
Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols.The onset of rapid degradation is called the knee point, and forecasting it is important for the safe and economically viable use for batteries.In this article, we propose a data-driven method that uses automated feature selection to produce inputs for a Gaussian process regression model that estimates changes in battery health, from which the entire capacity fade trajectory, knee point, and end of life may be predicted. The feature selection procedure flexibly adapts to varying inputs and prioritizes those that impact degradation. For the datasets considered, it was found that calendar time and time spent in specific voltage regions had a strong impact on the degradation rate. The approach produced median root mean square errors on capacity estimates under 1%, and also produced median knee point and end of life prediction errors of 2.6% and 1.3%, respectively.
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