An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots
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Bibliographic record
Abstract
In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it