State-of-Charge Prediction of Degrading Li-ion Batteries Using an Adaptive Machine Learning Approach
Bibliographic record
Abstract
State of charge (SOC) estimation of degrading batteries is important for battery energy storage systems (BESS) employed in power system applications and electric vehicles. This paper aims to propose a comparative analysis for data-driven models such as linear regression (LR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) to estimate the battery SOC at various temperatures and loading levels considering the state of health (SOH) of the battery. The historical data in which the cells are degraded from SOH of 100% to 60% are employed to extract the correlated features with the SOC. The models are retrained and adaptively updated based on the new SOH and prepared to estimate the SOC at the current SOH. The results demonstrate that GPR and RF models have the best performance. The mean absolute error of less than 0.0223 and 0.0204 have been achieved for RF and GPR, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".