Battery Aging Mechanisms Under Different Fast Charging Protocols: A Comparative Study on State of Health Estimation
Bibliographic record
Abstract
An accurate State of Health (SOH) estimation is crucial for ensuring the safe and efficient operation of electric vehicles (EVs). However, accurately estimating SOH in real-world applications is challenging due to diverse aging mechanisms, which result in varying battery characteristics and complicate the development of a universally applicable SOH estimation model. To address this issue, this paper investigates the aging characteristics of four INR21700 Samsung 30T cells subjected to different fast-charging protocols. By analyzing the incremental capacity (IC) curves, we identify specific features that effectively represent the aging status across different aging mechanisms. Utilizing these universal features, we develop a linear regression model (LRM) capable of adapting to various aging mechanisms for SOH estimation. The LRM is trained using data from a single cell and tested on the remaining cells. For the cell with the most similar aging trend, the Mean-Absolute-Error (MAE) is 0.94%, with an R2value of 0.99. Even for the cell with the most distinct aging trend and mechanism, the MAE is 1.70%, with an R2value of 0.94. These results demonstrate the robustness and adaptability of the proposed LRM for SOH estimation under diverse aging conditions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".