Performance Comparison of Different Power Management Control Strategies for a Hybrid Fuel Cell/Battery Vehicle
Bibliographic record
Abstract
Power management systems are one of the most important components in modern hybrid vehicles. They are needed to optimize the operation of the hybrid system components. In this paper, a model for a fuel cell/battery vehicle is developed using PSAT and then tested with four power management control strategies utilizing the driving cycle of Amman city, the capital of Jordan. The main components of the hybrid vehicle are a PEM fuel cell, battery, and a brushless dc motor. PEM fuel cells are popular due to their good start up, high power density, and low operating temperature. The role of the battery in a hybrid system is to boost the system power during start-up and transient events in addition to storing the energy recovered from the braking process. The developed hybrid vehicle model is designed and configured so that it matches the power, acceleration, and maximum speed of a midsized vehicle powered by an internal combustion engine. The proposed control strategies are the thermostat strategy, fuel cell optimized strategy, load following strategy and fuzzy logic strategy. All four control strategies are implemented in simulation utilizing PSAT. The simulation results indicate that the best performance in terms of fuel economy is achieved by the load following control strategy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".