A Robust and Simple Long Horizon Health Estimation of Lithium-ion Batteries Using NARX Recurrent Neural Network
Bibliographic record
Abstract
State of health (SOH) prediction is critical in battery management systems to ensure the reliability and safety of battery operation for further cycle running. Electric vehicle applications require an accurate SOH estimation at a low computational burden. Machine Learning approaches have been successful in precise battery health prognostic. Most of the recent studies introduces hybrid structure for accurate state estimation. This study presents a model based on a solo nonlinear autoregressive with external input (NARX) network for Lithium-ion batteries (LIBs). Validation on the NASA dataset shows excellent performance, with a root mean square error of less than 3% and a mean absolute error of less than 2% for validation batteries. Therefore, the method can accurately predict LIBS’s SOH based on historical data at lower computational complexity than the hybrid model. Therefore, this model is promising and practical for online applications for long-term prediction.
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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 | 0.001 |
| 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".