Simultaneous State and Parameter Estimation of Li-Ion Battery With One State Hysteresis Model Using Augmented Unscented Kalman Filter
Bibliographic record
Abstract
An online technique for simultaneous state and parameter estimation of an electric or hybrid vehicle battery using an augmented unscented Kalman filter (AUKF) is proposed. Battery state of charge (SOC), the prime state variable, is estimated. In addition the battery internal resistance, time constants, and resistance of the resistance-capacitance (RC) pairs are also estimated. While the two RC pairs of the equivalent circuit capture voltage dynamics, the hysteresis effect will capture the difference in the polarization of the electrodes between charge and discharge. A separate state representing the dynamics of the hysteresis voltage is included in the nonlinear state-space representation of the lithium ion battery model. The parameters are judiciously chosen to keep the overall estimation technique robust enough. It is demonstrated that the inclusion of parameters in a state-space representation of a battery model can generate better SOC estimation accuracy and the claim is corroborated with correlation between experimental and simulation results.
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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".