Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a reconfigurable battery pack, the connections among cells can be changed during operation to form different configurations. This can lead a battery, a passive two-terminal device, to a smart battery that can reconfigure itself according to the requirement to enhance operational performance. Several hardware architectures with different levels of complexities have been proposed. Some researchers have used existing hardware and demonstrated improved performance on the basis of novel optimization and scheduling algorithms. The possibility of software techniques to benefit the energy storage systems is exciting, and it is the perfect time for such methods as the need for high-performance and long-lasting batteries is on the rise. This novel field requires new understanding, principles, and evaluation metrics of proposed schemes. In this article, we systematically discuss and critically review the state of the art. This is the first effort to compare the existing hardware topologies in terms of flexibility and functionality. We provide a comprehensive review that encompasses all existing research works, starting from the details of the individual battery including modeling and properties as well as fixed-topology traditional battery packs. To stimulate further research in this area, we highlight key challenges and open problems in this domain.
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.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it