Optimal powertrain component sizing of a fuel cell plug-in hybrid electric vehicle using multi-objective genetic algorithm
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it