Lithium-ion Battery State-of-Health Estimation via Histogram Data, Principal Component Analysis, and Machine Learning
Bibliographic record
Abstract
Lithium-ion batteries are widely used in electric vehicle powertrain systems. As batteries age, their state of health (SOH), indicated by their usable capacity and power capability, decreases. For reliable battery operation, accurate estimation and prediction of SOH are essential. This paper proposes an algorithm for estimating battery capacity SOH from an open-source fast charging dataset with many different charge profile types. Histogram data is created from the measured time domain data and fed into a feedforward neural network (FNN). To capture the impact of different charge profiles on aging, current and state of charge (SOC) are multiplied together to create an additional synthetic input to the estimator. To reduce the number of inputs to the FNN to only those that contain valuable information, we use principal component analysis to reduce the total number of inputs by 80%. An SOH algorithm is proposed that can estimate capacity throughout the battery's life with a 1.03% root mean square percentage error (RMSPE) and 0.68% mean absolute percentage error (MAPE).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".