Fuel cell‐based topologies and multi‐input DC–DC power converters for hybrid electric vehicles: A comprehensive review
Bibliographic record
Abstract
Abstract In the last few decades, the utilization of fuel cells (FCs) in the automotive industry has created much attention due to easy use, modular structure, and higher efficacy. In the future, technological evolutions reveal that FC driven electric vehicles (EVs) will grow at a rapid pace and will become an excellent alternative to conventional vehicles. This paper discusses a detailed topological classification of the FC‐based hybrid electric vehicle (FCHEV). In these FCHEVs, one of the critical elements is the DC–DC power converter unit. The hybridization of FCs with the other power sources requires more converter units that make the system complex. A multi‐input DC–DC power converter is used to connect more than one energy source to reduce the system's complexity and improve the overall system efficacy. In this survey, numerous articles have been considered and examined vividly. An assessment of present and future scenarios of FCs based power source topologies and multi‐input DC–DC power converter topologies used in HEV is presented. This survey provides a deep insight into the topic for the researchers and engineers working in this field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".