Online Estimations of Li-Ion Battery SOC and SOH Applicable to Partial Charge/Discharge
Bibliographic record
Abstract
Estimating the state of health (SOH) and state of charge (SOC) of lithium-ion batteries is crucial for increasing the battery lifetime and performance. Many estimation methods are offline and require large datasets for training. The majority of online estimation methods either take too much time or need a full discharge or charge cycle. In this article, a fast online SOH estimation method that can work with partial charge/discharge is introduced. Only two consecutive partial discharge intervals are used to estimate the battery equivalent circuit model parameters and the open-circuit voltage (OCV). By comparing the estimated OCV curve at each interval with a reference or datasheet OCV curve, the battery capacity and, therefore, its SOH and SOC are accurately estimated. It is shown that updating the OCV reference curve based on temperature readings will provide more accurate results. NASA degradation dataset is used to validate the proposed method and the average reported root-mean-square error is below 1% for SOH and 1.07% for SOC.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".