A Novel ARD-GPR Approach for Li-Ion Battery SoH Estimation Using Frequency Response Analysis
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Bibliographic record
Abstract
As the use of lithium-ion batteries continues to grow, a large number of these batteries are reaching the end of their first life. Making informed decisions about battery retirement, second-life applications, and fair market value requires an accurate estimation of the battery's state of health (SoH). This paper presents a data-driven method for SoH estimation using features extracted from the frequency response of the battery in the form of electrochemical impedance spectroscopy (EIS) data. A novel model based on Automatic Relevance Determination Gaussian Process Regression (ARDGPR) is proposed, identifying the most important EIS features for accurate SoH prediction. Additionally, the paper introduces the design of a hardware platform capable of real-time EIS measurement and on-board SoH estimation. The proposed approach supports reliable battery health assessment for first- and second-life applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it