Plug-in hybrid electric vehicle charging: Current issues and future challenges
Bibliographic record
Abstract
There has been an increasing attraction towards plug-in hybrid electric vehicles (PHEVs) within the auto industry. However, high battery prices and short life spans have led to growing interest in development of advanced charging techniques and algorithms. In this paper, the characteristics of batteries used in PHEVs, which include Pb-acid, Ni-Cd, Ni-MH, Li-ion, and Li-polymer are reviewed, and different charging methods such as constant voltage, constant current, pulsed charging and burp charging are investigated. Termination methods such as time, voltage, voltage drop (dv/dt), current, and temperature are also discussed. Suitable charging algorithms for these battery types will be reviewed and evaluated. Battery charging issues such as cell equalization, state of charge estimation and thermal management will be discussed. Finally, future research trends of charging of PHEVs are described.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".