Active Battery Balancing for Battery Packs
Bibliographic record
Abstract
Abstract In electric vehicle applications, it is necessary to use series strings of batteries since the required voltage is higher than the one that can be obtained from a single battery. Due to several factors, imbalance of batteries in these battery systems is usual and an important factor that has to be taken into account. Many balancing methods have been developed with a lot of different advantages, but all of them also have a lot of disadvantages such as complexity and/or high cost, which are the common problems that can be found in most of these equalization methods. In the present work, a low cost and very simple equalization method is proposed, in which a novel control is applied to a shunting transistor topology. It allows the transistors to regulate the amount of current that goes through each battery cell in the string depending on their State of Charge (SOC), during the charging process. This control ensures that the least charged cells to be charged faster, and the most charged ones to be charged more slowly. Design criteria are discussed and simulation results are carried out in a generic battery low power application which proves the control method. Fast equalization with a low complexity and cost is obtained
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".