Improving Efficiency Through Adaptive Internal Model Control of Hydrogen-Based Genset Used as a Range Extender for Electric Vehicles
Bibliographic record
Abstract
This paper addresses a hydrogen-based generator (genset) adaptive control used as a range extender of a battery electric vehicle. Based on a commercially available gasoline genset, this hydrogen-based generator can use a mixture of gasoline and hydrogen in which the proportion of gasoline varies between 0% and 100%. This hybrid energy system is controlled by an onboard energy management system that splits the electric power demand between the battery and the genset. Given the genset power profile, a maximum efficiency tracking module was designed to provide optimal operating conditions (engine speed and electric power) to a real-time controller. To tackle the genset nonlinearities, an adaptive controller based on the internal model control approach is designed and successfully validated. In addition, a comparative study with an industrial-based control method indicates that the proposed approach is effective and can achieve significant improvement in genset efficiency.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".