State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)
Bibliographic record
Abstract
A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".