Equivalent Circuit Parameterization from Electrochemical Impedance Spectroscopy Data for Accurate Battery Degradation Prediction using Convolution Neural Network
Bibliographic record
Abstract
This paper presents a deep learning-based framework for the automated extraction and classification of equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data, aiming to enhance lithium-ion battery (LIB) diagnostics. EIS provides detailed insights into internal battery mechanisms, such as charge transfer, diffusion, and double-layer capacitance, by analyzing frequency-dependent impedance responses. Traditional interpretation methods are labour-intensive and limited in scalability. To address this, a one-dimensional convolutional neural network (1D-CNN) is employed to classify EIS spectra into four distinct ECM classes using features derived from real and synthetic datasets. The model architecture incorporates hierarchical convolutional layers, dropout, batch normalization, and global average pooling, achieving a classification accuracy of 95.65% on the test set. The predictions align closely with Nyquist plot characteristics of each ECM, validating the interpretability and robustness of the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".