Recursive Least Square Estimation Approach to Real-Time Parameter Identification in Li-ion Batteries
Bibliographic record
Abstract
In this paper, we consider the problem of equivalent circuit model (ECM) parameter identification in Li-ion batteries. Accurate estimation of the ECM parameters is critical for the safety, efficiency and reliability of the battery system. Existing approaches to solve this problem depend on information and parameters, such as, battery capacity, state-of-charge (SOC) and open circuit voltage (OCV) characterization parameters. Such reliance on other parameters makes the ECM identification less accurate. In this paper, we present a real-time approach to ECM identification. The proposed approach relies only on the measured voltage across the battery terminal and current through the battery. Also, the proposed approach is unaffected by the amount of hysteresis in the battery. Further, robustness in parameter identification is achieved through the inclusion of the measurement noise covariance matrix. The proposed algorithm was tested on simulated as well as real world battery data and found to be accurate within 1% uncertainty.
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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 | 0.002 |
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".