Optimization of a PEM Fuel Cell System for Low-Speed Hybrid Electric Vehicles
Bibliographic record
Abstract
Design optimization is performed by presenting a systematic method to obtain the optimal operating conditions of a Proton Exchange Membrane (PEM) fuel cell system targeted towards a vehicular application. The fuel cell stack model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada and one that is widely used by industry. Empirical data obtained from tests of PEM fuel cell stacks are used to determine the empirical parameters of the fuel cell performance model. Based on this stack model, a fuel cell system model is built in MATLAB. Included in the system model are heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components. The modified ADVISOR vehicle simulation tool is used to integrate the New York City Cycle (NYCC) drive cycle and vehicle model to determine the power requirements and hence the load cycle of the fuel cell system for a low-speed fuel cell hybrid electric vehicle (LSFCHEV). The optimization of the powerplant of this vehicle type is unique. The vehicle model has been developed in the work to describe the characteristics and performance of an electric scooter, a simple low-speed vehicle (LSV). The net output power and system exergetic efficiency of the system are maximized for various system operating conditions using the weighted objective function based on the load cycle requirement. The method is based on the coupling of the fuel cell system model with three optimization algorithms (a) sequential quadratic programming (SQP); (b) simulated annealing (SA); and (c) genetic algorithm (GA). The results of the optimization provide useful information that will be used in future study on control algorithms for LSFCHEVs. This study facilitates research on more complex fuel cell system modeling and optimization, and provides a basis for experimentation to verify the fuel cell system model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".