A review of lithium-ion battery state of health and remaining useful life estimation methods based on bibliometric analysis
Bibliographic record
Abstract
In recent years, research on the state of health (SOH) and remaining useful life (RUL) estimation methods for lithium-ion batteries has garnered significant attention in the new energy sector. Despite the substantial volume of annual publications, a systematic approach to quantifying and analyzing these contributions is lacking. This study focuses on selecting pertinent literature related to lithium-ion battery SOH and RUL estimation from CNKI and WOS databases, spanning January 2010 to December 2023. Employing bibliometric tools such as VOSviewer and CiteSpace, we conduct visual analyses to elucidate the current state, development trends, and research frontiers in this domain. Our examination encompasses scholarly activity, year-wise literature distribution, international collaboration networks, structural dissemination, and journal contributions. The findings indicate an upward trend in annual publication output, with China, the United States, the United Kingdom, and Canada at the forefront of collaborative research efforts. China is increasingly recognized as a pivotal hub for global scholarly partnerships. Notably, Harbin Institute of Technology, Beijing Institute of Technology, Chongqing University, Chinese Academy of Sciences, and Beijing Jiaotong University are the top institutions in China and the world in terms of publications. The Journal of Energy Storage emerges as a prominent periodical, acclaimed both domestically and internationally for its rigorous standards and high-quality articles. Based on the research content from CNKI and WOS, VOSviewer clusters the main research directions into three themes: aging mechanisms, SOH estimation methods, and RUL prediction methods. Keywords such as ‘online estimation’, ‘hybrid models’, and ‘artificial neural networks’ feature prominently, signaling a strong emphasis on artificial intelligence strategies. The study concludes with a prospective outlook on imminent research trajectories regarding the health and longevity estimations of lithium-ion batteries, highlighting the critical need for ongoing innovation and collaboration in this essential field.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.076 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".