Accurate Impedance Spectroscopy of Rechargeable Li-ion Batteries
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a commonly used method for evaluating battery health. It involves applying a high-frequency excitation signal to the battery and analyzing the resulting voltage response to generate its impedance spectrum. Traditional EIS techniques generally treat the battery as a linear time-invariant system, assuming that, under small signal perturbations and resting conditions, the impedance spectrum reliably represents the battery’s behavior. However, this paper demonstrates that the presence of the battery’s opencircuit voltage (OCV) invalidates the LTI assumption, even when a zero-mean excitation signal is used on a fully rested battery. Specifically, we show that OCV variations introduce nonlinearity in the voltage response, significantly affecting the impedance spectrum in the low-frequency range. To address this limitation, we utilize a novel approach that helps remove the OCV’s contribution from the measured voltage response. This correction allows for a more precise impedance analysis by directly addressing the nonlinearity caused by the OCV. The effectiveness of the proposed method is demonstrated using data from a battery simulator.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".