Passive and Active Coupling Comparison of Fuel Cell and Supercapacitor for a Three‐Wheel Electric Vehicle
Bibliographic record
Abstract
Abstract The desire to reduce the power electronics related issues has turned the attentions to passive coupling of powertrain components in fuel cell hybrid electric vehicles (FCHEVs). In the passive coupling, the fuel cell (FC) stack is directly connected to an energy storage system on the DC bus as opposed to the active configuration where a DC‐DC converter couples the FC stack to the DC bus. This paper compares the use of passive and active couplings in a three‐wheel FCHEV to reveal their strengths and weaknesses. In this respect, a passive configuration, using a FC stack and a supercapacitor, is suggested first through formulating a sizing problem. Subsequently, the components are connected in an active configuration where an optimized fuzzy energy management strategy is used to split the power between the components. The performance of the vehicle is compared at each case in terms of capital cost and trip cost, which is composed of FC degradation and hydrogen consumption, and total cost of the system per hour. The obtained results show the superior performance of the passive configuration by 17% in terms of total hourly cost, while the active one only results in less degradation rate in the FC system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".