Real-time power hardware-in-the-loop emulation of a parallel hybrid electric vehicle drive train
Bibliographic record
Abstract
One of the main challenges in the development of hybrid electric vehicles (HEVs) is control and co-ordination of several power sources. Power electronic emulators for drive trains provide effective and economic ways to test and validate control strategies in real-time. This paper proposes a real-time emulator for parallel hybrid electric vehicles. The major mechanical and electrical parts of a typical parallel hybrid electric vehicle power-train (the engine drive train and the motor drive train) is modeled mathematically and is emulated in real-time using power hardware-in-the-loop (PHIL). The individual sub-systems are modeled and locally controlled to maintain the required performance and control modes such as speed and torque. Real-time simulation was done in Matlab-Simulink and DS-1103 real-time controller and the results are presented. Voltage source inverters are used as power amplifiers to emulate the characteristics of the individual drive trains. The voltage source inverters are controlled by the same DS-1103 controller in rapid control prototype (RCP) mode. Experimental results with power hardware-in-the-loop emulator is presented for the validation of the proposed scheme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".