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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".