Hybrid Electrical Circuit Model and Deep Learning-Based Core Temperature Estimation of Lithium-Ion Battery Cell
Bibliographic record
Abstract
Aiming to improve the reliability and durability of lithium-ion battery (LIB) systems, a hybrid electrical circuit model (ECM) and two-dimensional grid long short-term memory (2-D GLSTM) neural network (NN)-based cell core temperature estimation technique is proposed in this article to leverage their respective strengths. The ECM is used to estimate the total heat generation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> ) inside the cell using the measured physical parameters such as voltage, current, and temperature. Furthermore, the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> and other external measurements are used by the 2-D GLSTM NN (deep learning (DL) approach) to estimate the core temperature of the battery cell. An experimentally validated Kalman filter (KF) and equivalent electrothermal model (EETM) are used to generate a large quantity of training and testing data, under a wide range of dynamic loading and ambient temperature to validate the proposed concept. The results showed that the proposed technique can achieve root-mean-squared-error of less than 1% with unknown test data which is quite satisfactory for practical applications. A comparative study with the state-of-the-art techniques in core temperature estimation showed that the proposed scheme performed better. The highly accurate prediction results under a comprehensive operating condition confirm the reliability and generalization ability with solid robustness to the external uncertainties.
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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 | 0.000 | 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 | 0.001 |
| 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".