A novel online adaptive fast simple state of charge estimation for Lithium Ion batteries
Bibliographic record
Abstract
This paper proposes a novel simple adaptive and online approach to estimate the state of charge (SOC) in Lithium Ion (Li-Ion) batteries based on a new model parameter identification method. First, a novel discrete model for the Li-ion battery is developed. This model is the key step in the development of the proposed parameter estimation algorithm. The estimated parameters are used for on-line calculation of the battery's open circuit voltage (VOC) that is required for SOC estimation with no prior knowledge of battery parameters. The paper then proposes a moving window lease mean square approach to adaptively update the estimated parameters in a very fast and accurate manner. The SOC estimation will be updated at the end of every window cycle. The proposed method for SOC estimation provides a simple, fast, comprehensive, and precise estimation capable to track the changes of the model/battery parameters. Unlike other estimation strategies, only battery terminal voltage and current measurements are required.
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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 | 0.000 |
| 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".