An Adaptive and Fast Health Estimation of Lithiumion Batteries Under Random Missing Data
Bibliographic record
Abstract
Data-driven techniques have been widely used in estimating batteries’ health state; however, the accuracy of these approaches heavily relies on the quality and quantity of data collected. It is imperative to have an accurate method for evaluating battery health in unreliable industrial environments where sensor measurements may be inaccessible. This paper presents a nonlinear autoregressive with exogenous inputs (NARX) estimator model for lithium-ion batteries (LIBs), taking into account the random missing measurements that batteries are exposed to. For fast and efficient health diagnostics for online applications, missing observation occurrence is addressed by randomly removing some sample data and evaluating the model based on available measurements using the aging features and their exponential moving average. The promising results reveal the model’s efficiency under various random data missing rates of 1% to 30%, with MAEs and RMSs below 0.6%.
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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 | 0.001 |
| 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".