Supervisory Real-Time Multidomain Modeling and Hardware Emulation of Fuel-Cell Hybrid Electric Bus Behavioral Transients
Bibliographic record
Abstract
Real-time device-level multi-domain emulation can provide an accurate insight into behavioral transients of the hydrogen fuel-cell hybrid electric bus (HEB). However, the conventional electromagnetic transient (EMT) simulation suffers from the computation burden caused by the complex multi-domain subsystems. This paper develops a hybrid recurrent neural network (RNN) and EMT method for device-level multi-domain emulation for fuel-cell and battery HEB. Two recurrent neural networks (RNN) are designed and trained to create device-level models of permanent magnet synchronous motor (PMSM) and the modular multilevel converter (MMC), respectively. The IGBTs’ behavioral transients and thermal performance are integrated into the RNN-based MMC model. Moreover, the EMT models represent the energy behaviour of onboard fuel-cell stacks and battery stacks. The proposed multi-domain hybrid models are implemented on the Xilinx Versal™ adaptive compute acceleration platform (ACAP), where multiple AI engines and the programmable logic deal with the RNN and EMT models, respectively. The real-time hardware emulation is carried out at the time-step of 0.1 μs for device-level transients. The results show that the hybrid model has 96.3% accuracy; furthermore, it significantly reduces the HEB emulation time compared to conventional EMT methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".