A Novel Hybrid Physics-Based and Data-Driven Approach for Degradation Trajectory Prediction in Li-Ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries have been widely used in electric vehicles. To ensure safety and reliability, accurate prediction of the battery’s future degradation trajectory is critical. However, early prediction capability and adaptive prediction capability under various battery aging conditions remain two main challenges. Either physics-based or data-driven methods have their advantages and limitations. In this study, a novel hybrid method that combines the physics-based and data-driven approaches is proposed to achieve early prediction of the battery capacity degradation trajectory. This framework consists of three steps. First, to improve the generality of the method, a hybrid feature is extracted using an electrochemical model and measured voltage data. Second, the clustering algorithm is adopted to divide battery degradation data into different clusters, and the data augmentation technique is used to enrich the training dataset. Finally, the training dataset in each cluster is used to train the sequence-to-sequence deep neural network, and the future degradation trajectory can be predicted. The proposed method provides accurate predictions using only 20% of training data, and it has strong robustness under noisy input. Validation results under different aging conditions show that the mean absolute percentage errors of capacity degradation trajectory and remaining useable cycle life are below 2.5% and 6.5%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".