A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers
Bibliographic record
Abstract
We have developed a battery hardware–in–the–loop (HIL) setup, which can expedite the design and evaluation of power management controllers for hybrid electric vehicles (HEVs) in a novel cost– and time–effective manner. The battery dynamics have a significant effect on the HEV power management controller design; therefore, physical batteries are included in the simulation loop for greater simulation fidelity. We use Buckingham's Pi Theorem in the scaled–down battery HIL setup to reduce development and testing efforts, while maintaining the flexibility and fidelity of the control loop. In this paper, usefulness of the setup in parameter identification of a simple control–oriented battery model is shown. The model is then used in the power management controller design, and the real–time performance of the designed controller is tested with the same setup in a realistic control environment. Test results show that the designed controller can accurately capture the dynamics of the real system, from which the assumptions made in its design process can be confidently justified.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".