Name
Title | ||
|---|---|---|
A Novel Machine Learning Method Based Approach For Li-Ion Battery Prognostic And Health Management |
Abstract | ||
|---|---|---|
Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is introduced. Finally, we apply the attention mechanism to further improve the accuracy of the algorithm. Compared with traditional neural network methods, the method proposed in this paper has higher efficiency and accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2947843 | IEEE ACCESS |
Keywords | DocType | Volume |
Prediction algorithms, Lithium-ion batteries, Heuristic algorithms, Prognostics and health management, Machine learning algorithms, Monitoring, RUL prediction, variational mode decomposition (VMD), extreme learning machine (ELM), prognostic and health management (PHM), grey wolf optimizer (GWO), differential evolution (DE), attention mechanism | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaming Fan | 1 | 0 | 0.68 |
| Jianping Fan | 2 | 0 | 0.34 |
| Feng Liu | 3 | 46 | 10.34 |
| Jiantao Qu | 4 | 0 | 1.35 |
| Ruofeng Li | 5 | 0 | 0.34 |