Optimal powertrain component sizing of a fuel cell plug-in hybrid electric vehicle using multi-objective genetic algorithm
Bibliographic record
Abstract
Considerable efforts have been made recently to develop a completely zero-emission and highly fuel efficient vehicle. Due to clean and efficient power generation, the hydrogen fed fuel cell vehicle (FCVs) has received considerable attention. However, major obstacles such as cost of the hydrogen infrastructure, driving range, and cost of the fuel cell greatly influence FCV development. At the same time, proper utilization of grid power, along with a modified electrical system infrastructure, would encourage automakers to envisage plug-in versions of fuel cell vehicles. This paper presents the optimal powertrain component sizing of a fuel cell plug-in hybrid electric (FC-PHEV) vehicle, comprised of a fuel cell with electrolyser, Ni-MH battery as secondary energy storage, and a propulsion motor. Such a PHEV architecture provides an additional degree of freedom, as the grid power can be used to recharge batteries, or for the electrolysis of water, to generate hydrogen and oxygen, which increases the driving range of vehicle as well as the overall powertrain efficiency. Hence, the overall performance and efficiency are much superior when compared to ordinary PHEV or FC-HEV powertrains. This paper uses a small vehicle power train for modelling and simulation purposes. Optimal sizing of the power train components using multi-objective genetic algorithm will be presented. Moreover, overall vehicle performance and fuel economy for different driving loads will also be analysed. Finally, an overall cost analysis will also be presented.
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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 | 0.001 |
| 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".