Optimal power flow for hybrid ultracapacitor systems in light electric vehicles
Bibliographic record
Abstract
This work demonstrates a predictive power optimization algorithm to control the power mix in a hybrid energy storage system, consisting of an ultracapacitor module and a lithium-ion battery pack for light electric vehicle applications. The algorithm uses a state-based approach, organized as a probability-weighted Markov process to predict future load demands. Decisions on power sharing are made in real-time, based on the predictions and probabilities of state trajectories along with associated system losses. A real-time global optimizer is then used to control the appropriate power mix using dc-dc converters. The full hybrid storage system, along with the mechanical drivetrain is implemented and validated experimentally on a 350 W, 50 V system with a programmable drive-cycle having a strong regenerative component. It is shown that the HESS system runs more efficiently and captures the excess regenerative energy that is otherwise dissipated in the mechanical brakes due to the battery's limited charge current capability.
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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 | 0.000 |
| 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) | 0.000 | 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".