Integrated Convex Speed Planning and Energy Management for Autonomous Fuel Cell Hybrid Electric Vehicles
Bibliographic record
Abstract
Fuel cell hybrid electric vehicles (FCHEVs) have zero harmful emissions, fast refueling times, and long driving ranges. Autonomous FCHEVs add benefits such as collision avoidance and driver convenience yet increase vehicle energy usage due to sensors and computation. Thus, it is important to optimize vehicle speed trajectories and the energy management strategy (EMS) of autonomous FCHEVs to minimize energy use. This article uniquely proposes to achieve this goal using convex optimization, with detailed vehicle loss calculations that can be run in real time. The two novel approaches proposed are: 1) the successive method, which solves the speed problem then the EMS problem, and 2) the integrated method, which uniquely feeds back the fuel cell efficiency to the speed algorithm to solve the problem in an iterative integrated manner. The simulation results show that the integrated method uses 0.19% to 2.37% less hydrogen than the successive method on short drive cycles with varying accessory loads, and 10.12%–21.62% less hydrogen than an arbitrary constant speed profile. On longer real-world drive cycles, the integrated method reduces hydrogen use by 1.43% to 2.82% just through speed optimization. Compared to a dynamic programming benchmark, which is not implementable in real time, the integrated method uses less than 1% more hydrogen.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".