Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are used to power a range of applications starting from portable consumer electronics to electric vehicles and grid-tied energy storage systems. Now, with the increasing application of LIB in high power and sophisticated applications, it is of great significance to predict the remaining useful life (RUL) for reliable operation and to protect the battery pack from unwanted incidents including catastrophic failure. Real-time information on RUL is essential to predict battery failure condition resulting in effective prevention or at least reduction of the damage that may cause by the battery failure. Moreover, accurate RUL is extremely helpful for scheduling routine maintenance and necessary replacement at the end of its useful life. Consequently, RUL prediction has become a topic of interest to researchers. There are several RUL estimation techniques proposed in the last decade where machine learning (ML)-based techniques showed superiority in terms of accuracy, adaptability, and modeling. Therefore, ML-based RUL prediction methods are comprehensively reviewed based on their essential performance parameters in this paper. A detailed discussion on the issues, challenges, trends, and future research scopes are also presented to provide clear guideline to the researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".