A novel online energy management strategy for multi fuel cell systems
Bibliographic record
Abstract
This paper addresses the design of an energy management strategy (EMS) for a multi-stack fuel cell system (MFCS). In this regard, firstly, two power allocation strategies, namely Daisy Chain and Equal Distribution have been developed and compared in characteristics terms. Subsequently, a novel adaptive strategy is proposed to split the power between the fuel cells and the battery by utilizing the demanded power, state of charge (SOC) of the battery, maximum power and efficiency point of each fuel cell. In a MFCS, each fuel cell shows variable performances in different operating conditions depending on its specific ageing, material, and external factors. The purpose of this study is to ensure an equal level of degradation for each fuel cell and to make them operate in an efficient zone, with the assistance of an online identification method as well as an adaptive power strategy. Simulations have been conducted in Matlab-Simulink environment. In this work, a mechanistic fuel cell model is employed to imitate the behaviour of a real MFCS and a semi-empirical model, coupled with an adaptive recursive least square (ARLS) to predict the maximum power (MP) and maximum efficiency (ME). The results of the proposed strategy show noticeable improvements in the fuel economy.
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 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".