Intelligent EV Charging Control and Management: From Microscale Battery Cell to Macroscale Grid Synergy
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 rapid increase in electric vehicle (EV) use highlights the need for advanced charging infrastructure and strategies. This survey offers an in-depth exploration of EV charging, emphasizing its control and optimization dimensions. The analysis covers a broad spectrum, from the intricacies of individual battery cell management to the challenges of grid integration. The paper reviews various control methods and optimization techniques, addressing key factors like charging efficiency, battery life, safety measures, temperature control, and cell balancing. It also discusses the role of charging stations and energy storage systems in improving charging efficiency, grid stability, and handling peak demands. By examining the interaction between the grid and EV charging, the study explores grid limitations, power quality, and demand-side management. This comprehensive survey improves our understanding of EV charging control and optimization, paving the way for future research in this field.
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 | 0.001 | 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 | 0.001 |
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 it