Modeling, parameterization, and state of charge estimation of Li-Ion cells using a circuit model
Bibliographic record
Abstract
This paper presents a general procedure applied for modeling, parameter identification, and state of charge (SOC) estimation of a Li-Ion battery cell. The paper explains a battery tester with a number of experiments conducted to investigate the cell physical properties. Dynamics of the Li-Ion cell are modeled using an equivalent circuit model, whereas parameters of the model are calculated using particle swarm optimization. This method minimizes the output error that is the difference between the simulated output from the model and the measured terminal voltage. The provided equivalent circuit model with optimized parameters was used for SOC estimation. Two different state estimation methods have been applied to estimate the cell SOC based on real-time measurements. The estimation methods include the extended Kalman filter (EKF), and the novel smooth variable structure filter (SVSF). The SVSF method was used as it can produce more accurate state estimates for dynamic systems with modeling and parametric uncertainties. This paper compares the performance of these two estimators for real-time SOC estimation using tester data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".