Stability Enhancement of Hybrid Fuel Cell-Battery-Supercapacitor Systems Using a Hamiltonian Control Approach
Bibliographic record
Abstract
This paper presents a Hamiltonian control law to enhance the stability and performance of a hybrid energy storage system (HESS) composed of a proton exchange membrane (PEM) fuel cell, lithium-ion battery, and supercapacitor. The control design is based on port-Hamiltonian system theory, enabling dynamic energy management among sources while maintaining global system stability. A nonlinear control law is developed using damping-injection techniques to regulate the DC bus voltage, ensure optimal power sharing, and respect power and energy constraints of each component. The strategy allocates fast dynamics to the supercapacitor, medium response to the battery, and slow dynamics to the fuel cell, achieving efficient energy coordination. Experimental validation is performed using a dSPACE controlled test bench with real-time monitoring. Results confirm that the proposed method ensures fast transient response, smooth voltage regulation, and robust operation under sudden load variations.
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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 | 0.000 | 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".