Online estimation of model parameters and state-of-charge of Lithium-Ion battery using Unscented Kalman Filter
Bibliographic record
Abstract
For the operation of Autonomous Mobile Robot (AMR) in unknown environments, accurate estimation of internal parameters and consequently precise prediction of the battery state of charge (SoC) are critical issues for power management. Battery performance can be affected by factors such as temperature deviation, discharge/charge current, Coulombic efficiency losses, and aging. Thus, in order to increase the model accuracy, it is important to update the model parameters online. In this paper, the Unscented Kalman Filter (UKF) is employed for the online estimation of the Lithium-Ion battery model parameters and the battery SoC based on the updated model. The proposed method is evaluated experimentally, and the results are compared with that of the Extended Kalman Filter (EKF). The comparison with the EKF shows that UKF provides better accuracy both in battery parameters estimation and the battery SoC estimation.
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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".