Repurposed Battery Energy Storage System for use in applications of Renewable Energy Generation
Bibliographic record
Abstract
Electric and hybrid electric vehicles’ batteries not only have great potential for alleviating the world’s gasoline consumption problem, but may also stand poised to secure the world’s renewable energy generation. Electric and hybrid electric vehicles’ batteries that have reached the end of their cycle life in vehicles may still have the capacity to be repurposed into stationary utility energy storage. However, the phenomenon known as battery aging must be given careful consideration in the construction of a repurposed battery energy storage system. The battery aging phenomenon reduces the battery’s nominal voltage, capacity and current rating, while increasing its internal resistance. These factors were taken into consideration for the development of the Repurposed Battery Energy Storage System (RBESS). The system utilizes a method called Multi-Level Interlaced Pulse Charging (MLIPC) which was developed for the RBESS to manage the battery’s voltage, current, and energy to extend the useful cycle life of the batteries. The repurposed battery energy storage system has been modeled in PSCAD/EMTDC and tested in a constructed hardware implementation of the system.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".