Robust Hamiltonian Energy Control Based on Lyapunov Function for Four-Phase Parallel Fuel Cell Boost Converter for DC Microgrid Applications
Bibliographic record
Abstract
Rapid developments in hydrogen fuel cell (FC) energy and DC microgrid systems have extended the applications of multiphase parallel interleaved step-up converters for stabilizing DC bus voltages. DC microgrid applications include vehicle systems, shipboard power systems, and more electric aircraft, which generate power at low voltage levels. The cascade architecture of a power converter in a DC microgrid may cause large oscillations and imbalance given that converters considered as loads have constant power load characteristics. In this work, output DC bus voltage stabilization and current sharing of a multiphase parallel-interleaved-FC boost converter is presented. The proposed robust controller with added integrator action is based on the Hamiltonian-Lyapunov function. The efficacy and robustness of the designed controller were successfully authenticated by experimental results obtained using a 2.5 kW prototype FC converter (via four-phase parallel-interleaved boost converters) and the dSPACE MicroLabBox platform. The main source of the FC is based on a fuel reformer engine that converts fuel methanol and water into H2gas in a polymer-electrolyte-membrane-FC stack (50 V, 2.5 kW).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".