Hybrid electric excursion ships power supply system based on a multiple energy storage system
Bibliographic record
Abstract
This study presents the multiple energy storage elements usability for ships using a passive hybrid topology. The considered hybridisation is based on a passive parallel topology connecting NiMH batteries and SuperCapacitors to a DC power distribution by a bidirectional DC/DC converter. The overall propulsion architecture is a hybrid series system where an engine–generator group is the main energy source and the multiple energy storage system (ESS) answer the intermittent power demanded by the on‐board loads. First, the multiple ESS is sized using voltages, storage elements’ characteristics, and typical power demand profile. Thereafter, a control scheme of this hybridisation is deduced through a cascade of current and voltage linear controllers. The energy management strategy based on the stability of the DC power distribution voltage and the inherent storage elements’ characteristics is fully addressed in order to reduce weight and space on‐board, fuel consumption, pollution, and optimise the global efficiency. Experimental results show that the engine–generator fulfil a constant power, meanwhile the multiple ESS stabilises the DC‐link voltage with unknown power demand profile. The effectiveness of the proposed passive hybrid topology with reduced control layer complexity based on the DC‐link voltage stabilisation as an energy management strategy is validated.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".