Online parameter identification of a retrofitted hydrogen genset for maximum efficiency
Bibliographic record
Abstract
This work presents an electric vehicle range extension system based on a hydrogen genset which is obtained by retrofitting a 5kW gasoline genset equipped with two generators. The power provided by the hydrogen genset is used to extend the battery pack autonomy through a serial topology. This genset has the potential to be less expensive and more robust to extreme low operating temperature than a fuel cell. In addition, it can operate at different hydrogen-air mixtures. To improve the genset energy efficiency, the operating conditions that lead to a maximum efficiency need to be selected online. We propose a two-step method in which the genset efficiency behavior is represented by a parametric smooth surface in the first step whilst the operating conditions for a maximum efficiency is selected during the second step. These smooth surface parameters, identified using the recursive least square method, provided a root mean square error of the deviation between the estimated and the measured efficiency time profiles less than 2.5 ×10−3. Using the line search method, the optimal operating condition has been identified and 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".