Hybrid Vehicle Simulation System with discrete-event modeling and simulation
Bibliographic record
Abstract
Embry-Riddle Aeronautical University (ERAU) is one of fifteen schools across the United States and Canada that qualify to compete in the EcoCAR competitions. One of the major tasks of the ERAU EcoCAR team, besides to produce a working vehicle that fits the required hybrid vehicles, is to ensure predictions of the car's performance. In this paper we present the use of the Discrete-Event System Specification (DEVS) formalism to simulate the physical hybrid car in its current state. We have modeled the car and manipulated various inputs and conducted simulation results that in theory match with those of the physical car. The proposed Hybrid Vehicle Simulation System produces scientifically sound results in theory that can be verified and validated. Development is performed at a system level, identifying the major contributing components, connecting those components as it is in the physical car, and simulating the various inputs and outputs of each component. The behavior of each component is analyzed as data is injected into it and the results are documented. Test plans are formulated to test each component (unit) under different conditions. The produced simulation will be incrementally replaced with actual hardware components of the car. This work benefits from a number of modeling and simulation concepts such as: component-oriented model-based development, model-continuity, and incremental replacement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".