Study and Investigation of Energy Management Techniques Used in Electric/Hybrid Electric Vehicles
Bibliographic record
Abstract
Many pollution-related issues are raising due to the usage of conventional internal combustion engines (ICEs) vehicles. Electric Vehicles/ Hybrid electric vehicles (EVs/HEVs) are the finest solutions to overcome those problems associated with ICE-based vehicles. The EVs are introduced with a signal energy source (SES), which is not a successful attempt, especially during transient vehicles, driving, etc. Multiple energy sources (MES) EVs are introduced to attain better performance than the SES vehicles, which is obtained by combining two sources like battery/fuel cells, ultracapacitor. In this contest, energy management (EMNG) plays a vital role in sharing the load to the sources as per the EVs requirement. In the case of MES-based EVs, the controller always plays a significant role in the related EMNG system because it is the key factor in improving vehicle efficiency. In this article, a study has mainly been done related to several conventional, intelligent controllers and control algorithms to do the proper EMNG between sources present in the EV.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".