Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF
Aiming at the problems of complex modeling and large errors in the prediction of remaining useful life (RUL) of lithium-ion batteries, a novel RUL prediction method was proposed. Firstly, the battery historical capacity was decomposed into a set of intrinsic mode functions (IMFs) and one residue...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2211 |
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| Summary: | Aiming at the problems of complex modeling and large errors in the prediction of remaining useful life (RUL) of lithium-ion batteries, a novel RUL prediction method was proposed. Firstly, the battery historical capacity was decomposed into a set of intrinsic mode functions (IMFs) and one residue based on the complementary ensemble empirical mode decomposition (CEEMD). Based on the permutation entropy (PE) and root mean square error (RMSE), an optical low-pass filter was established to eliminate the random fluctuation and noise of the raw capacity. Secondly, the adaptive Kalman filter (AKF) was used to update the parameters of the Autoregressive (AR) model. Finally, a probability density function (PDF) was obtained based on Monte Carlo (MC) simulation, which was used to evaluate the uncertainty of RUL prediction. The experimental analysis on the NASA data set shows that the CEEMD-AKF method can not only reduce the modeling complexity, but also can effectively improve RUL prediction accuracy. |
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| ISSN: | 1007-2683 |