Health Monitoring of Lithium-Ion Batteries Using Dual Filters
Bibliographic record
Abstract
Accurate estimation of a battery’s capacity is critical for determining its state of health (SOH) and retirement, as well as to ensure its reliable operation. In this paper, a dual filter architecture using the Kalman filter (KF) and the novel sliding innovation filter (SIF) was implemented to estimate the capacity and state of charge (SOC) of a lithium-ion battery. NASA’s Prognostic Center of Excellence (PCOE) B005 battery data set was selected for this experiment based on its wide use in academia and industry. This dataset contains cycling data of a 2 Ah lithium-ion battery until its capacity was measured at 1.3 Ah or less. The dual polarity equivalent circuit model (DP-ECM) was selected for modeling. The model parameter values were estimated using the least squares (LS) algorithm. Under normal operating conditions, both the dual-KF and dual-SIF performed similarly in terms of estimation accuracy. However, an uncertainty case was considered where the filters were subjected to rapid changing dynamics by cutting the data by 300 cycles. In this case, the battery capacity root-mean-square error (RMSE) for the dual-KF and the proposed dual-SIF were 0.1233 and 0.0675, respectively. Under rapidly changing dynamics and faulty conditions, the dual-SIF shows better convergence and robustness to disturbances.
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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".