Real-time Point-to-Point Parameter Tracking for Fault Prognosis of Lithium-ion Batteries Using Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
With the increasing concerns of lithium-ion battery (LIB) failure, which can lead to fire and catastrophic failure in electric vehicles (EVs), this paper proposes an online parameter tracking-based fault prognosis strategy focusing on small frequency windows within the EIS spectrum. Early detection of faults remains an unsolved challenge in the practical application of LIBs, especially in high-power applications such as EVs. The proposed concept leverages the possibility of real-time tracking of internal battery parameters by identifying frequency windows. In most cases, faults and failures originate from a single cell and then propagate to the entire LIB pack. Therefore, an experimental setup consisting of one LIB cell, a battery cycler, and electrochemical impedance spectroscopy (EIS) is used to demonstrate the effectiveness of the proposed concept through battery aging and during faults. Furthermore, the results provide guidelines on the most sensitive battery parameters for predicting failures in practical applications. This information will help in reducing EIS measurement time, data storage, and computational costs without compromising prediction 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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".