Deep Learning‐Based Surrogate Model for Lithium‐Ion Battery Behavior Prediction with Extreme Fast Charging Case Study
Bibliographic record
Abstract
The rapid growth in lithium‐ion battery applications has increased the demand for efficient and accurate models capable of predicting battery behavior. Electrochemical models provide high fidelity but are typically too computationally intensive for real‐time control or large‐scale simulations. Herein, a deep learning‐based surrogate model (DL‐SBM) designed to forecast battery performance using historical data, significantly reducing computational costs while maintaining accuracy is introduced. The surrogate models are trained on synthetic datasets generated by an electrochemical simulator under diverse charging scenarios, and optimized through adaptive hyperparameter tuning. The resulting models accurately predict critical battery parameters such as voltage, temperature, and state of charge. Furthermore, a reinforcement learning case study focused on extreme fast charging demonstrates that the DL‐SBM achieves accuracy comparable to physics‐based simulators, while operating ≈225 times faster, requiring less memory, and ensuring robust performance under extreme conditions. These results underline the suitability of the DL‐SBM for real‐world battery management 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".