Remaining useful life prediction of lithium‐ion battery based on an improved particle filter algorithm
Bibliographic record
Abstract
Abstract Because lithium‐ion batteries are the main power source of industrial electronic equipment, their degradation process modelling and remaining useful life (RUL) prediction problems have attracted wide attention. The particle filter (PF) method has been successfully applied to suppress the model uncertainty and predict the RUL of the lithium‐ion battery. In order to further enhance the stability of the PF method and realize a more satisfactory prediction result, a RUL prediction method based on the hybrid algorithm, which combines the PF algorithm and extended unbiased finite impulse response (EFIR) filter, is proposed. Firstly, the state space model of capacity degradation for the lithium‐ion battery is established, and the model parameters are estimated by the extended Kalman filter (EKF) algorithm. Secondly, a preliminary battery capacity is predicted by using a regularized particle filter. The preliminary predictions with large deviations are diagnosed and repaired by combining the EFIR filter and diagnostic strategy. Finally, the optimized RUL prediction results of the lithium‐ion battery are extrapolated based on the failure threshold. The experiment results demonstrate that the proposed method has good stability and accuracy in predicting the RUL of a lithium‐ion battery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".