Minimizing the Operating Cost of a Hybrid Multi-Stack Fuel Cell Vehicle Based on a Predictive Hierarchical Strategy
Bibliographic record
Abstract
The operating cost of multi-stack fuel cell (FC) hybrid electric vehicles (HEVs) is notably affected by the energy management strategy (EMS). For this purpose, this study proposes a predictive hierarchical (PH)-EMS to decrease the multi-stack FC-HEV operating cost. The PH-EMS consists of two levels. The first level is a Sugeno-type fuzzy logic (FL)-EMS that determines the number of active FCs for participation in an optimization level based on the vehicle's future velocity. The second level is the model predictive control (MPC) approach, which distributes the optimal power among FCs and the battery based on the number of active FCs and predicted velocity in the prediction horizon. To evaluate the effectiveness of the proposed EMS, the results are compared to a rule-based (RB) EMS. The results indicate that the total operating cost of the PH-EMS is 55.504% lower compared to RB-EMS.
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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 | 0.000 |
| 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".