Understanding the Effect of Measurement Noise in Fast Pulse-Based Impedance Spectroscopy
Bibliographic record
Abstract
Accurate impedance estimation is essential for reliable battery diagnostics and modeling. Discrete Fourier transform (DFT)–based impedance estimation is widely adopted in electrochemical impedance spectroscopy (EIS) for battery characterization due to its simplicity and computational efficiency. However, the influence of measurement noise on such DFTbased impedance computation remains insufficiently understood. This paper develops a statistical framework that analytically quantifies how zero-mean Gaussian noise propagates through DFT-based impedance estimation. A closed-form expression for the bias of impedance estimates is derived, and a rectangular pulse excitation signal is employed to evaluate its performance for fast EIS applications under this framework. Simulation studies on a two time-constant RC model of the battery reveal that measurement noise and excitation design jointly influence estimation accuracy. For pulse excitation, bias amplification occurs near the high-frequency spectral zero-crossings, where the excitation spectrum approaches zero magnitude. By excluding a guard-band of impedance estimates surrounding these zerocrossings, the peak bias in both real and imaginary components is reduced by approximately 70% at 60 dB signal-to-noise ratio (SNR). Furthermore, increasing the pulse ON duration from 10 ms to 100 ms reduces the low-frequency bias by about 55%, strengthening the low-frequency signal content and improving the overall estimation accuracy.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".