Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation
Bibliographic record
Abstract
While Li-ion batteries have proven long lifetimes, an accurate assessment of the battery ca-pacity and its remaining life cannot yet be made using current Battery Management Systems (BMS) devices. Battery sensors used in BMS typically mon-itor voltage, current and temperature of the battery, in order to predict the state of health (SoH) of the battery. SoH is a measure that indicates the remaining capacity that had been affected by degradation. Information obtained by monitoring voltage, current and temperature are often not sufficient to predict SoH. In this study we captured extra information from interfacial layers of the battery through applying Electrochemical Impedance Spectroscopy (EIS) and employed a XGBoost-based machine learning approach to train our models. The results show that SoH of batteries can be predicted with 90% accuracy, 95% confidence and 82% reliability. Additionally, it was shown that accuracy could be maintained with little to no change even when the number of features was dramatically reduced and the sample size was minimal, thus making this method very practical for embedded EIS/AI based solutions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".