Design and control of multiphase interleaved boost converters-based on differential flatness theory for PEM fuel cell multi-stack applications
Bibliographic record
Abstract
This article is focused on the development of an energy management algorithm applied to a multi-stack fuel cell (FC) system for DC microgrid applications. To guarantee the performance of the FC stacks, the current ripple is reduced by employing multiphase interleaved boost converters. A proposed advanced control technique of the multi-stack with multiphase converters for the proton exchange membrane (PEM) FCs is estimated based on a differential flatness approach, in which it can track the power demand in real-time. Furthermore, the differential flatness based-control can ensure the balance of the DC bus voltage of the DC microgrid when load disturbance occurs. The flatness-based energy management strategy is based on both inner current loops (control of the multi-stack PEMFC through their multiphase interleaved boost converters) and outer voltage loop (DC bus voltage regulation). Compared to classic PI controllers mainly based on the linearization of the system to obtain the transfer function (making complex its application), the flatness-based theory leans on time-domain making it easier its use for various applications while ensuring good performances. To validate the proposed control structure, an FC converter system (5 kW) is realized and validated in the laboratory. For hydrogen production, the methanol FC system has consisted of a reformer engine that changes water mixed methanol liquid into hydrogen to supply FC stacks (ME2Power Fuel Cell System: 50 V, 5 kW). The proposed control algorithm is tested experimentally by using a dSPACE controller board platform. Simulation and test bench results authenticate the excellent performance during load cycles in DC microgrid.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".