Hybrid solar-electric cart efficiency enhancement: A bibliometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.058 | 0.115 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it