Development of a software platform for a plug-in hybrid electric vehicle simulator
Bibliographic record
Abstract
Abstract Electricity use for transportation has had limited applications because of battery storage range issues, although many recent successful demonstrations of electric vehicles have been achieved. Renewable biofuels such as biodiesel and bioethanol also contribute only a small percentage of the overall energy mix for mobility. Recent advances in hybrid technologies have significantly increased vehicle efficiencies. More importantly, hybridization now allows a significant reduction in battery capacity requirements compared to pure electric vehicles, allowing electricity to be used in the overall energy mix in the transportation sector. This paper presents an effort made to develop a Plug-in Hybrid Electric Vehicle (PHEV) platform that can act as a comprehensive alternative energy vehicle simulator. Its goal is to help in solving the pressing needs of the transportation sector, both in terms of contributing data to aid policy decisions for reducing fossil fuel use, and to support research in this important area. The Simulator will allow analysing different vehicle configurations, and control strategies with regards to renewable and non-renewable fuel and electricity sources. The simulation platform models the fundamental aspects of PHEV components, that is, process control, heat transfer, chemical reactions, thermodynamics and fluid properties. The outcomes of the Simulator are: (i) determining the optimal combination of fuels and grid electricity use, (ii) performing greenhouse gas calculations based on emerging protocols being developed, and (iii) optimizing the efficient and proper use of renewable energy sources in a carbon constrained world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".