Maximizing Driving Range for Fuel Cell Range Extender Vehicles With Fixed Energy Storage Costs
Bibliographic record
Abstract
Industry and researchers are investigating both battery electric vehicles (BEVs) and fuel cell hybrid vehicles (FCHVs) for the future of sustainable passenger vehicle technology. While BEVs have clear efficiency advantages, FCHVs have key benefits in terms of refueling time and energy density. This article first proposes the concept of a fuel cell range extender vehicle (FCREV) that uses whole-day driving prediction (WDDP) control, which uses driver destination inputs to determine whether the planned driving trips that day will exceed the useable battery energy capacity. If so, the fuel cell is turned on at the start of the day. The benefit of WDDP control is that a smaller, lower cost fuel cell can be used to greatly extend the driving range since the fuel cell can charge the battery during both driving and parked periods of the day. Furthermore, this research proposes a fast analytical optimization algorithm for designing a WDDP-FCREV to maximize the range on a given drive cycle for a set cost. The results show that an optimized WDDP-FCREV can greatly exceed the range of the same-cost BEV, by 105%–150% for no H2 refueling and by 150%–250% when H2 refueling is allowed every 4 h.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".