Developing an Online Data-Driven State of Health Estimation of Lithium-Ion Batteries Under Random Sensor Measurement Unavailability
Bibliographic record
Abstract
Data-driven approaches have demonstrated remarkable accuracy in battery’s state of health (SOH) estimation; however, they are susceptible to data quality and quantity. Therefore, an accurate data-based battery health estimation method is highly desirable in an unreliable industry environment when sensors’ random measurements unavailability is ubiquitous. Successful training under random data unavailability becomes a difficult task to undertake. Therefore, the main challenge is how an offline trained model can be reliable and accurate under random sensors’ measurements unavailability. This article develops an accurate SOH estimation model based on nonlinear autoregressive with exogenous inputs recurrent neural network for lithium-ion batteries whose features’ measurements are subjected to different random missing observations. To evoke the uncertainty of sensors’ measurements in online health diagnostic, missing observation occurrence is addressed by randomly eliminating sample data and then evaluating the model on the available measurements. Therefore, it does not require any imputation strategy for missing values. The accuracy of the estimator model is guaranteed when extracted underlying features are fused by adding their exponential moving average as the health features. The experimental results on two different datasets, Oxford and Toyota, under different battery chemistry and working operations demonstrate that the mean absolute errors (MAEs) and RMSs are well bounded below 2.70% and 3.10% for different random data missing rates of 1%–30%. It is a promising prediction model for numerous industrial applications with a high probability of random data unavailability.
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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".