A Simplified Design, Control and Power Management of Fuel Cell Vehicles
Bibliographic record
Abstract
In the automotive industry, design processes always start with modeling and simulation for fast and cost-effective design prototype validation. Thereafter these simulations must be validated. Electric vehicles feature a wide range of components from different domains with controllers. One of the challenges in modeling and simulation of these vehicles lies in the integration of these diverse components into a single simulation environment. In this paper, an electric vehicle model is implemented along with an integrated hybrid storage system in MATLAB/Simulink. The hybrid energy storage system encompasses a proton exchange membrane (PEM) fuel cell system and a Lithium-ion battery, which are linked together via a current-regulated DC-DC converter. The simulated fuel cell vehicle model allows us to explore different power management strategies. Specifically, in order to improve the fuel cell stack efficiency and avoid damages to the fuel cell due to peak loads, a simple power-flow control strategy that allows us to operate the fuel cell on demand using a bypass contactor is evaluated. In order to validate the fuel cell vehicle model and demonstrate the effectiveness of the proposed power control strategy, numerous simulations are performed using highway drive cycles. Finally, the simulation results are compared with that of a traditional fuel cell vehicle 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".