Optimal performance of a full scale li-ion battery and li-ion capacitor hybrid energy storage system for a plug-in hybrid vehicle
Bibliographic record
Abstract
A semi-active hybrid energy storage system, consisting of a Li-ion battery pack, dc/dc converter, and Li-ion capacitor pack is developed for a range extended plug-in vehicle application. The vehicle has a series-parallel drivetrain with two electric motors, a gas engine, gearbox, and a clutch to allow the engine to run decoupled from the gearbox in range extending mode. The peak dc electrical requirement of the electric drivetrain is about 175kW, which is similar to the peak power capability of the developed hybrid energy storage system. A model of the prototype hybrid energy storage system, which has the Li-ion capacitor pack connected directly to the motor drive's dc bus and the battery pack connected to the Li-ion capacitor pack via a dc/dc converter, is developed and used to determine the optimal power split between the battery and Li-ion capacitor packs. A dynamic programming algorithm is used to determine the optimal power split, with the optimization goals of reducing energy storage system loss, maximizing regenerative braking energy capture, and minimizing motoring power limiting. The hybridized system is shown to reduce battery pack losses and increase vehicle range compared to a system only utilizing the battery pack.
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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 | 0.001 |
| 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".