Battery Storage Sizing in a Retrofitted Plug-in Hybrid Electric Vehicle
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
This paper develops a simulation-based framework for optimal sizing of the additional energy storage required to retrofit a hybrid electric vehicle (HEV) to a plug-in hybrid electric vehicle (PHEV). Simulations are conducted on a vehicular model developed for a midsize sedan (Toyota Prius) using a new weekly vehicle-usage profile constructed for average driving and most probable parking times based on the data collected in the city of Winnipeg (Canada). Three battery technologies that are commercially available for electric vehicle propulsion are used in the simulations to determine the optimal sizing of the battery storage, given the constraints on the volume of the battery pack for lowest cost. Overnight-charging and opportunity-charging scenarios are also implemented in the simulation, and their impact on the optimal sizing is discussed.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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