Decentralized Implementation of an Optimal Energy Management Strategy in Interconnected Modular Fuel Cell Systems
Bibliographic record
Abstract
This paper aims at designing a novel decentralized energy management strategy to optimally split the power in a modular fuel cell system (FCS). The FCS is composed of two fuel cells (FCs) in a parallel structure and a battery pack. The proposed decentralized strategy consists of two layers. Initially, a local optimization problem is solved by means of auxiliary problem principle (APP) method at each time step for each of the fuel cell modules (FCMs). Subsequently, the obtained values are broadcasted to the near FCM neighbors. At each step, the APP utilizes the power values and Lagrange parameters of the previous step shared by the sub-problem neighbors to find the solution that is the reference power for each FC. Compared to the centralized form of the APP algorithm, besides the modularity point of view, the proposed strategy is able to converge to the optimal answer faster. The final results indicate that the performance of the proposed strategy is very close to the results achieved by dynamic programming (DP).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".