Genetic algorithm based optimal powertrain component sizing and control strategy design for a fuel cell hybrid electric bus
Bibliographic record
Abstract
Recent trends shows that hydrogen powered fuel cell vehicles (FCVs) are gaining universal attention, because of the need for more fuel-efficient vehicles. Advancement in fuel-cell technology has ignited interest in all-electric propulsion systems. Regardless of some drawbacks in terms of number of electrical storage components being used and relatively larger capacity of on-board energy storage required, compared to hybrid electric vehicles, all-electric propulsion systems offer the most effective solution for achieving zero emissions drive-trains. Both the sizing of powertrain components as well as the control strategy affects vehicle performance, due to their interdependency. Moreover, during sizing, various design constraints should also be satisfied simultaneously. Hence, optimization of fuel cell vehicle components can be simply treated as a multi-objective constrained nonlinear optimization. This paper considers a fuel cell powered electric transit bus, with battery and ultracapacitor as additional sources of power, to improve the overall drive performance and efficiency. Optimal sizing of the powertrain components is carried out, in conjunction with optimizing the overall control strategy design, through a suitably devised multi objective genetic algorithm method. The main goal is to achieve higher fuel economy with minimum power train cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".