Open-Circuit Voltage Modelling Toolbox for Battery Management Systems
Bibliographic record
Abstract
Open circuit voltage-state of charge (OCV-SOC) characterization is essential to battery management systems. The past two decades have seen the development of numerous models and approaches for OCV-SOC characterization. While some complex models may represent the OCV-SOC relationship more accurately than others, their advantage may be lost in practical systems that are restricted to a limited amount of computing resources. Also, some advanced OCV-SOC models require extensive data processing and coding. An automatic way to characterize the OCV-SOC curve of a battery and to obtain its parameters is currently unavailable. Therefore, this paper presents a Battery Modeling Toolbox capable of modelling the OCV-SOC curve using linear models from the literature. The toolbox allows the selection of OCV-SOC models from several options based on the requirements of the end user. The toolbox described in this paper will be a useful asset to researchers and scientists developing new battery chemistries; battery management system designers; academics; and graduate student researchers.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.033 |
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".