Using Real-Time Simulation in Hybrid Electric Drive and Power Electronics Development: Process, Problems and Solutions
Bibliographic record
Abstract
This paper highlights the usage of real-time simulation in the development process of hybrid electric drive and other vehicle components that make use of power electronics. The paper objective is to demonstrate the various usages of real-time simulation technologies in the V-cycle design process of hybrid vehicle named Faster-Than-Real-Time (FTRT), Rapid Control Prototyping (RCP) and Hardware-In-the-Loop (HIL). The paper explains these solutions in the context of design of fuel cell hybrid electric vehicle systems and power converters. The paper shows that the fast dynamic of electric systems requires powerful real-time simulators as well as adapted solvers and simulation techniques. Special models are required to accurately simulate in real-time the power converter and drives found in hybrid electric vehicles. The paper demonstrates this by the use of basic examples followed by the real-time simulation of a fuel cell hybrid vehicle (FCHV). The FCHV case makes use of the two main applications of real-time simulation technologies, RCP and HIL simulation, interconnected in a complete fuel cell hybrid vehicle application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".