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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-06-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2211 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387667762970624 |
|---|---|
| author | CHEN Xiang XIA Fei |
| author_facet | CHEN Xiang XIA Fei |
| author_sort | CHEN Xiang |
| collection | DOAJ |
| description |
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. |
| format | Article |
| id | doaj-art-82afe9fec05f47b8b444370d188fe4c8 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-82afe9fec05f47b8b444370d188fe4c82025-08-20T03:51:29ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832023-06-012803283610.15938/j.jhust.2023.03.004Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKFCHEN Xiang0XIA Fei1College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaCollege of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China 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.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2211lithium-ion batteriesremaining useful lifeautoregressive modepermutation entropymonte carlo simulation |
| spellingShingle | CHEN Xiang XIA Fei Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF Journal of Harbin University of Science and Technology lithium-ion batteries remaining useful life autoregressive mode permutation entropy monte carlo simulation |
| title | Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF |
| title_full | Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF |
| title_fullStr | Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF |
| title_full_unstemmed | Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF |
| title_short | Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF |
| title_sort | remaining useful life predictionmethod for lithium ion batteries based on ceemd akf |
| topic | lithium-ion batteries remaining useful life autoregressive mode permutation entropy monte carlo simulation |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2211 |
| work_keys_str_mv | AT chenxiang remainingusefullifepredictionmethodforlithiumionbatteriesbasedonceemdakf AT xiafei remainingusefullifepredictionmethodforlithiumionbatteriesbasedonceemdakf |