Hybrid solar-electric cart efficiency enhancement: A bibliometric analysis
Bibliographic record
Abstract
The present study involves the development of an electric cart, with future research aiming to enhance its efficiency by creating a hybrid solar-electric cart. To achieve this goal, a bibliometric analysis of electric vehicle (EV) batteries is required. This study aims to identify research gaps in EV batteries through Bibliometric Analysis, utilizing Scopus Analyze and VOSViewer to analyze 1,276 documents obtained from the Scopus database, including articles (49.7%), conference papers (43.3%) and various other publications such as reviews, book chapters, reports, short surveys, notes, books, erratum, and editorials. The analysis reveals a substantial surge in EV battery research and publications within the Scopus database since 2013, and this trend is projected to continue until the end of 2023. Based on researchers’ affiliations, Chinese institutions have ranked first in contributions, followed by institutions from the United States, India, the United Kingdom, and Canada. Surprisingly, the University of Warwick secured the top among research institutions, with the Beijing Institute of Technology claiming the second position. The VOSViewer analysis generated six keyword clusters relevant to EV battery research. Of particular interest is Cluster 5, which emphasizes the significance of battery management techniques, establishing efficient battery swapping stations, optimizing energy management strategies, and exploring the role of EV batteries in building intelligent grids. These gaps identified in Cluster 5 will become the focal point for future research, especially concerning efficiency enhancement through developing a hybrid battery system capable of a hybrid solar-electric cart.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.124 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".