Robust Battery State-of-Charge Estimation in Presence of Model Uncertainties Using KalmanNet
Bibliographic record
Abstract
State estimation based on Kalman Filter (KF) has been widely considered for state-of-charge (SoC) prediction of lithium-ion (Li-ion) batteries. However, the KF is a model-based approach, and its performance declines when faced with modeling inaccuracies resulting from the nonlinear and timevarying nature of Li-ion batteries. To tackle the modeling uncertainties, a novel hybrid variant of KF is deployed, in which priori estimations are attained similar to the KF algorithm while the posterior estimations are obtained using a recurrent neural network (RNN) integrated into the KF architecture. The proposed method yields enhanced robustness in maintaining the accuracy of SoC estimations in the presence of model mismatches, e.g. due to the aging of batteries. In the proposed method, named KalmanNet, the RNN learns from battery data to predict the optimum Kalman gains for the minimization of SoC estimation error. The proposed method is trained and tested using actual battery data of a high-capacity pouch Li-ion cell replicating real-life driving cycles of electric vehicles. The significance of the results is twofold: 1-When exposed to a model mismatch of ±5%, the SoC estimation accuracy is improved by about 0.5% compared to EKF. 2-The size of required training data is reduced by 20% compared to similar machine learning algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".