Optimal Control of Li-Ion Hydrogen Fuel Cell Hybrid Vehicles
Bibliographic record
Abstract
Hydrogen fuel cells are poised to become the next major power generation technology for the automotive industry. Fuel cell hybrid vehicles have similar power trains to conventional series hybrid electric vehicles. The underlying hybrid control concepts used for conventional hybrid vehicles are still valid although it is unknown how well they will perform on fuel cell based vehicles since the fuel cell is a fundamentally different power source. \nThis thesis reviews several control strategies for fuel cell vehicles including a mode switching rule-based control strategy, a constant fuel cell output strategy and an adaptive variation of the equivalent consumption minimization strategy (A-ECMS) which has been modified for fuel cell vehicles. These strategies are implemented in simulation and evaluated against optimal strategies. The optimal strategies have been determined using convex optimization problem solving techniques. \nThe mode switching and constant fuel cell strategies have also been evaluated in real world testing on a fully functional, road safe, fuel cell powered SUV. The test vehicle was designed and built by the University of Waterloo Alternative Fuels Team (UWAFT) for the EcoCAR competition. \nThe simulation results demonstrate that near optimal fuel economies can be achieved through operating the fuel cell at near peak efficiency while the battery manages all major transients in the power demand. The constant fuel cell strategy demonstrates the highest fuel economy of all the tested strategies since it operates continually within this high efficiency region. The mode based strategy showed the worst results since the fuel cell would follow the transients of the power demand, pushing it out of the peak efficiency region. The simulation results were validated by the experimental results which showed similar relationships. The A-ECMS provided good results although they were lower than the constant fuel cell strategy. \nHydrogen fuel cell vehicles have the real potential to become the next major vehicle technology. Only by continuing to research every aspect of these vehicles needed to make them viable for consumer use can these vehicles ever replace the gasoline powered vehicles we use today.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".