Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors
Bibliographic record
Abstract
Lithium-ion battery (LIB) has been deployed for the electrification of the transport sector as a key strategy for climate change mitigation and adaptation. However, it has significant technical challenges such as thermal runaway, requiring a good understanding and accurate prediction of the LIB thermal behavior (heat generation rate). In this study, a novel hybrid approach using an Artificial Neural Network (ANN) is developed for predicting the heat generation rate with discharge current, output voltage, ambient temperature, cell surface temperature, and Depth of Discharge (DOD) as the feature vectors (inputs); where the DOD is estimated with an Extended Kalman Filter (EKF), and direct Coulomb Counting (CC) method, respectively. A shallow neural network utilizing the Marquette-Levenberg algorithm is designed and calibrated using over 8000 cases of the testing data. It is shown that the predicted heat generation rate of LIB agrees well with the experimental results with an accuracy of R > 0.995. Further potential of this hybrid data-based model is evaluated by simulating a thermal management system control and by introducing voltage and current sensor faults for diagnostic purposes. It is shown that, when compared to the experimental value, the relative error of the total heat output generated is less than 2% when there is no sensor fault, and greater than 50% and 25%, respectively, with an induced failure of the current and voltage sensor, demonstrating the ability to build accurate models relying solely on LIB discharge data for sensor diagnostics. This study highlights the combination of using battery thermal behavior with machine learning for real time battery system monitoring, controls and field diagnostics.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".