Application of Artificial Intelligence to State-of-Charge and State-of-Health Estimation of Calendar-Aged Lithium-Ion Pouch Cells
Why this work is in the frame
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Bibliographic record
Abstract
Accurate State-of-Charge (SOC) and State-of-Health (SOH) estimation of lithium-ion batteries (LIBs) is essential for the battery management system (BMS). For the first time, a feed-forward artificial neural network (ANN) has been used to estimate SOC of calendar-aged lithium-ion pouch cells. Calendar life data has been generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35°C and 60°C) and conditions (fully-discharged and fully-charged). The data has been obtained at various C-rates for duration of 10 months at one-month intervals. In order to include LIB hysteresis effect, two separate ANNs have been trained for charge and discharge data. The ANN have achieved a Root Mean Square Error (RMSE) of 1.17% over discharge data and 1.81% over charge data, confirming the ability of the network to capture input-output dependency. The calendar-aged battery data at various degradation conditions has been employed to train a new ANN to estimate SOH. The ANN has shown RMSE of 1.67%, demonstrating the network capability to estimate SOH. This study highlights the importance of considering aging effects in SOC estimates and the ability of ANN to include these effects efficiently.
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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 it