Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach
Bibliographic record
Abstract
Accurate, reliable, and robust prognosis of the state of health (SOH) and remaining useful life (RUL) plays a significant role in battery pack management for electric vehicles. However, there still exist challenges in computational cost, storage requirement, health indicators extraction, and algorithm design. This paper proposes a novel dual Gaussian process regression model for the SOH and RUL prognosis of battery packs. The multi-stage constant current charging method is used for aging tests. Health indicators are extracted from partial charging curves, in which capacity loss, resistance increase, and inconsistency variation are examined. A dual Gaussian process regression model is designed to predict SOH over the entire cycle life and RUL near the end of life. Experimental results show that the predictions of SOH and RUL are accurate, reliable, and robust. The maximum absolute errors and root mean square errors of SOH predictions are less than 1.3% and 0.5%, respectively, and the maximum absolute errors and root mean square errors of RUL predictions are 2 cycles and 1 cycle, respectively. The computation time for the entire training and testing process is less than 5 seconds. This article shows the prospect of health prognosis using multiple health indicators in automotive applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".