On Conversion of Hybrid Electric Vehicles to Plug-In
Bibliographic record
Abstract
The retrofit conversion of currently available hybrid electric vehicles (HEVs) to plug-in HEVs (PHEVs) is studied in this paper through experiments and simulations using the powertrain system analysis toolkit (PSAT). First, a rule-based fuzzy controller of the battery energy-management unit is developed to simulate different energy-management policies. Second, by modifying the energy-control strategy, the model of the conversion PHEV (C-PHEV) is verified with experiments. Finally, the C-PHEV model is used to simulate different battery energy-management control strategies. The results show improvement in fuel economy, whereas the energy-management controller discharges the power through the plug-in battery pack only when the state of charge of the base vehicle battery is close to its minimum value. This method keeps the advantage of driving in electric mode using a combination of two batteries and optimizing the use of regenerative braking capabilities, which is the main advantage of HEVs. It is also found that increasing the power threshold of the internal combustion engine (ICE) improves the performance of C-PHEV. Increasing the ICE power threshold increases the engine efficiency by running the engine in its efficient points. It also drives the vehicle in electric mode in higher power demands.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".