Tabular Open Circuit Voltage Modelling of Li-ion Batteries for Robust SOC Estimation
Bibliographic record
Abstract
The open circuit voltage (OCV) to state of charge (SOC) representation of batteries characterizes the electrode potential difference of the battery (i.e) the open circuit voltage as a function of the amount of charge the battery can hold. Traditionally, the OCV-SOC curve is represented by polynomial functions. The battery management system (BMS) conventionally stores the OCV-SOC curve in terms of coefficients of such poly-nomial functions. These coefficients are used for real-time SOC estimation based on measured or computed OCV. It is required to store the OCV-SOC parameters up to several decimal digit accuracy in order to precisely estimate the SOC. This demands high computing and memory resources to adequately represent the OCV-SOC curve. However, most practical BMS's are limited in terms of their memory, which means the parameters are often rounded before stored. The perils of rounding the OCV-SOC parameters are highlighted in this paper. Then, a systematic solution is proposed to create an alternative solution in the form of an OCV-SOC table, which can eliminate dependencies on system requirements. The proposed tabular approach is also robust to rounding compared to their parametric counterparts. A formal validation metric is evaluated to compare the robustness of the tabular model and the existing empirical model. It can be concluded that the proposed OCV-SOC table outperforms the traditional parametric models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".