A Dual-Domain Diagnostic Window for Aging Analysis of Lithium-Ion Batteries
Bibliographic record
Abstract
Accurate extraction of equivalent circuit model (ECM) parameters is essential for aging-aware battery management in lithium-ion batteries (LIBs). Electrochemical Impedance Spectroscopy (EIS) offers detailed insight into ohmic resistance, charge-transfer kinetics, and diffusion processes, but its onboard application is constrained by unstable measurement conditions. This work introduces a diagnostic window at 100% SOC immediately after the constant-voltage (CV) phase, where interfacial stabilization and kinetic relaxation yield quasi equilibrium suitable for reproducible impedance measurements. A short post-CV rest is included only as a verification step to confirm minimal voltage drift. Validation was performed on three cells representing pristine, moderately aged, and heavily aged states. GITT, conducted at a low C/25 rate, provided a laboratory benchmark, while EIS was carried out at 0%, 50%, and 100% SOC under controlled rests. Comparative analysis showed strong consistency in ohmic resistance across techniques, while EIS demonstrated superior resolution of charge-transfer and diffusion processes, particularly in aged cells, thereby making it suitable for real-time evaluation. These findings establish the CV-based diagnostic window as a reproducible, diagnostically rich, and onboard-compatible method for ECM parameter tracking and aging diagnostics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".