Dynamic Model of a Plug in Hybrid Electric Vehicle
Bibliographic record
Abstract
Renewable biofuels like biodiesel and bioethanol contribute only a small percentage of the overall energy mix for mobility. Electricity use for transportation has limited applications because of battery storage range issues. Recently, the trend of research leads in hybrid technologies with significant increase in vehicle efficiencies that created the Plug-in Hybrid Electrical Vehicles (PHEV) platform. A comprehensive alternative energy vehicle simulator is presently seen as an important contribution to the transportation issue, both in terms of contributing data to help policy to reduce fossil fuel use and to support research in this important area. The Renewable Energy Vehicle Simulator (REVS) allows analysing different vehicle configurations, control strategies and renewable and non-renewable fuel and electricity sources. The goal is to determine the optimal combination of fuels and grid electricity use and perform greenhouse gas calculations based on emerging protocols being developed, optimizing the efficient and proper use of renewable energy sources in a carbon constrained world. The simulation platform for REVS models the fundamental aspects of PHEV components, i.e. process control, heat transfer, chemical reactions, thermodynamics and fluid properties. In this work IDEAS simulates the internal combustion engine and chemical reactions to provide emissions out of engine. Matlab and Simulink is employed to simulate the dynamics of the vehicle.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".