A Robust Extended Kalman Filtering Approach for State of Charge Estimation in Batteries
Bibliographic record
Abstract
State of charge (SOC) estimation is a crucial challenge faced by battery management systems. Extended Kalman filter (EKF) based approaches have been widely explored in the literature for SOC estimation. The challenge with the EKF approach to SOC estimation is that the parameters of the underlying state-space model (SSM) are not perfectly known. Such uncertainty in the SSM may arise at both the model order and the model parameter levels. The SSM of the SOC estimation problem at the most reduced model order form is defined using close to ten parameters all of which suffer from uncertainties. A disadvantage of the EKF approach is that when the SSM parameters deviate from reality, the filter starts to produce incorrect SOC estimates unbeknown to the user. This paper presents a novel EKF approach to SOC estimation that is robust against model parameter uncertainties. The proposed robust EKF employs additional states to absorb the uncertainties in the model parameters and employs two metrics, both computed based on filter innovations, to detect model order uncertainty. The proposed approach is demonstrated using a battery simulator.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".