State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model
Bibliographic record
Abstract
State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This article proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based voltage-capacity (VC) (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) is extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. The results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".