A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation
Bibliographic record
Abstract
Accurate state-of-health (SOH) estimation is critical for lithium-ion batteries' safe and reliable operation. These batteries are widely used for commercial products, including smartphones, laptops, and electric vehicles. In this paper, we develop a convolutional neural network (CNN) based battery SOH estimation model trained to estimate SOH from constant current charge and discharge data. Aging data from four cells, each charged with a different fifteen-minute fast-charging current profile, is used to train and test the SOH estimation model. The model's accuracy is demonstrated by training with data from one fast-charging aging case and tested using the other three cases, which age at a considerably different rate. The results show that the method is quite robust when the tested cells have more than 80% SOH, with error typically within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{2}{\%}$</tex> and not exceeding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{3}{\%}$</tex> . However, the proposed method has limitations when trying to predict battery health below 80% or when trying to predict battery health from curves with different C-rates. The datasets and the code for the algorithm in this paper are available to download.
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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".