Design, Testing, and Validation of a Simplified Control Scheme for a Novel Plug-In Hybrid Electric Vehicle Battery Cell Equalizer
Bibliographic record
Abstract
In order to meet cost targets for hybrid electric (HEV), plug-in hybrid electric (PHEV), and all-electric vehicles (EV), an improvement in the battery life cycle and safety is essential. Recently, lithium batteries, in the form of lithium-ion, lithium-polymer, or lithium iron phosphate have been explored. Despite research initiatives, lithium-based batteries have not yet been able to meet steep energy demands, long lifetime, and low cost of vehicular propulsion applications. One practical approach to improve performance is to use power electronics intensive cell voltage equalizers, in conjunction with on-board energy storage devices. The purpose of this paper is to introduce a simplified control scheme, based on open-circuit voltage estimation, for a novel cell equalizer configuration, with the potential to fulfil expectations of the following: 1) low cost; 2) large currents; and 3) high efficiency. Issues, such as the limitations on maximum and minimum cell voltage, noise, and quantization errors, are explored. Finally, a comprehensive comparison between the theoretical test results and practical equalization test results is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".